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Computer Science Graphics

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Sunday 30 August

Graphics

As-Rigid-As-Possible Deformation of Gaussian Radiance Fields

Xinhao Tong, Tianjia Shao, Yanlin Weng et al. (+2 more)

3D Gaussian Splatting (3DGS) models radiance fields as sparsely distributed 3D Gaussians, providing a compelling solution to novel view synthesis at high resolutions and real-time frame rates. However, deforming objects represented by 3D Gaussians remains a challenging task. Existing methods deform a 3DGS object by editing Gaussians geometrically. These approaches ignore the fact that it is the radiance field that rasterizes and renders the final image. The inconsistency between the deformed 3D Gaussians and the desired radiance field inevitably leads to artifacts in the final results. In this paper, we propose an interactive method for as-rigid-as-possible (ARAP) deformation of the Gaussian radiance fields. Specifically, after performing geometric edits on the Gaussians, we further optimize Gaussians to ensure its rasterization yields a similar result as the deformed radiance field. To facilitate this objective, we design radial features to mathematically describe the radial difference before and after the deformation, which are densely sampled across the radiance field. Additionally, we propose an adaptive anisotropic spatial low-pass filter to prevent aliasing issues during sampling and to preserve the field with the varying non-uniform sampling intervals. Users can interactively employ this tool to achieve large-scale ARAP deformations of the radiance field. Since our method maintains the consistency of the Gaussian radiance field before and after deformation, it avoids artifacts that are common in existing 3DGS deformation frameworks. Meanwhile, our method keeps the high quality and efficiency of 3DGS in rendering.
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All authors Xinhao Tong, Tianjia Shao, Yanlin Weng, Yin Yang, Kun Zhou

Saturday 29 August

Graphics

ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reaction

Zhuoqiang Cai, Yujie Sun, Chaoyue Niu et al. (+4 more)

We propose ECHO for dyadic 3D facial motion generation under a strict dual-stream audio-only setting, formulating the problem as an asymmetric task involving speech-constrained articulation and one-to-many listener reactions. To address this asymmetry, ECHO decomposes motion into a deterministic anchor that captures stable speech-correlated structure and a stochastic residual that models the remaining one-to-many interaction dynamics. On top of this backbone, Motion Memory acts as a training-only regularizer during brief late-stage fine-tuning to provide local priors for weakly conditioned listening windows, while semantic-group scaling controls residual injection across expression, jaw, and neck. This design balances speaking-side articulatory fidelity with listening-side realism and diversity in a single generation process. Results from unified, state-wise, and ablation evaluations show that conversational 3D motion benefits from decomposing stable and uncertain components rather than applying stochasticity uniformly. ECHO provides a practical formulation and technical basis for deployable conversational digital humans under strict audio-only conditions.
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All authors Zhuoqiang Cai, Yujie Sun, Chaoyue Niu, Hongyun Yu, Zhiwen Chen, Chengfei Lv, Fan Wu

Graphics · Cross-listed from cs.CR

Asymmetric Phase Coding Video Watermarking

Guang Yang and Fengchen Liu

Existing video watermarking systems are symmetric: the party that can verify a mark holds the extractor weights or generator secret and can therefore also embed one. Benchmarks confirm the consequence, reporting that white-box forgery defeats all evaluated methods. We present a training-free video watermark that removes the shared secret. The signer embeds a complete Ed25519 signature into the phase spectrum of the chroma plane; any party holding the 32-byte public key and public per-video metadata verifies offline, with no model, no registry, and no network. The payload, 1024 bits of signed message with error correction, is an order of magnitude above common learned payloads and is carried by three design elements: a run-length temporal layout whose decoder identifies payload groups by correlation and never reads a frame index, a payload-free search that recovers scale, rotation, and translation from the carrier itself, and a closed-loop signing procedure that selects each video's embedding strength by self-verification through the unchanged public verifier. On 1000 uncurated real-world clips the system ships a verifying signature for 99.3% of the corpus and accepts a wrong public key zero times in 1000 attempts. An attack-aware acceptance gate yields embeddings that survive H.264 re-encoding at 100% and 50% rescaling at 97.4% on gated clips. The signature also verifies through a real display and capture loop, an axis absent from published evaluations.
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Graphics · Cross-listed from cs.CV

DReSG: Diffusion Residuals for Stylized Gaussian Splatting

Zhongliang Liu, Wenjie Liu and Yang Li

Reference-guided stylization of scenes represented by 3D Gaussian Splatting (3DGS) is important for efficient and controllable 3D content creation. Existing VGG-feature-based 3D stylization methods provide stable rendered-view optimization, but often under-represent expressive reference style cues; diffusion models offer stronger image priors, yet direct per-view or score-based diffusion guidance can lead to view drift, local artifacts, and hard-to-control appearance updates. We present DReSG, a 3D-grounded residual-feedback framework for stylized Gaussian splatting. DReSG represents attention-guided diffusion proposals as residual targets relative to the current render, and progressively absorbs these residuals into a shared Gaussian scene through multi-view Gaussian feedback. To make this feedback stable and controllable, DReSG modulates residual strength during target construction and combines coverage-aware view selection with conflict-filtered color updates during multi-view fitting. Extensive experiments demonstrate that DReSG achieves competitive reference-guided stylization while better preserving scene structure and cross-view stability. Our project page is available at https://vpx-ecnu.github.io/DReSG-website/.
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Friday 28 August

Graphics · Cross-listed from cs.CV

AgenticCADedit: A Stateful, Tool-Mediated Agentic Approach to Multimodal 3D CAD Editing

Saptarshi Neil Sinha, Mika Silvan Goschke, Paul Julius Kühn et al. (+2 more)

Computer-aided design is central to industrial manufacturing, and much of a designer's daily work consists of editing existing models from multimodal requests involving speech, sketches, and model interaction. Existing neural CAD approaches focus predominantly on unconditional or text-conditioned generation. The neuralCAD-Edit approach formalizes expert multimodal editing requests, but its iterative baseline refines a complete CAD program across attempts, executing each attempt from the original model in a stateless CAD environment. Every attempt must therefore reconstruct the entire edit from scratch, so partially correct progress is discarded rather than accumulated, and the model can neither inspect the geometry it has just produced nor selectively revert a single faulty operation. We present AgenticCADedit, which turns editing into a sequence of small, verifiable actions on a persistent CAD state instead of a single regenerated program. Rather than emitting one complete program, it applies incremental code steps that each commit to the session, inspects the resulting faces and edges, renders highlighted selections to verify that the intended region was addressed, and reverts individual operations when it was not. Subsequent actions therefore build on the geometry produced by earlier ones. Our approach improves on all metrics for all three evaluated LLMs (open-weight: qwen3.6-27b, gemma4-31b; proprietary: gpt-5.6-luna), with the largest gains for the weakest baseline model, qwen3.6-27b, whose validity rises from 51.0% to 94.8% and acceptance from 1.6% to 12.0%. A token-cost analysis with gpt-5.6-luna further shows $66.7$% fewer output tokens than neuralCAD-Edit, while $94.8$% of input tokens are served from the prompt cache.
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All authors Saptarshi Neil Sinha, Mika Silvan Goschke, Paul Julius Kühn, Arjan Kuijper, Michael Weinmann

Graphics · Cross-listed from cs.CV

mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis

Adnan Armouti, Yixuan Gao and Rajalakshmi Nandakumar

High-resolution 3D radar data is scarce. Commodity mmWave sensors use small antenna arrays that limit angular resolution to several degrees, and existing datasets provide only 2D range-azimuth maps or sparse point clouds rather than raw analog-to-digital converter (ADC) signals. Hardware scaling is expensive, synthetic-aperture scanning is impractical at fleet scale, and learned synthesis methods are bottlenecked by the very data shortage they aim to address. We present mmIR, an open-source differentiable frequency-modulated continuous-wave (FMCW) radar inverse renderer that fits a physics-based forward model to real captures and re-renders from dense virtual apertures to synthesize high-resolution 3D radar data. Because radar resolution is too coarse to recover geometry directly, mmIR performs LiDAR-assisted inverse rendering: using LiDAR-derived meshes as a geometric scaffold, mmIR optimizes per-vertex International Telecommunication Union (ITU) physics materials, vertex normals, and antenna beam patterns through end-to-end automatic differentiation of a phase-coherent multiple-input multiple-output (MIMO) forward model with multi-bounce propagation, polarization, and free-space diffraction. On seven outdoor and six indoor ColoRadar scenes, mmIR achieves 0.914 mean Pearson correlation on range-azimuth maps versus 0.307 for Sionna-RT. Scenes trained on a cascaded imaging radar transfer to a co-located single-chip radar without re-training (0.554 correlation), and dense virtual arrays (100x100 elements) produce single-frame 3D occupancy validated against LiDAR. Project page: https://mmwave-inverse-rendering.github.io/
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Graphics

Transparency Rendering in Computer-Aided Design: Methodologies, Trade-offs, and Challenges

Grigoris Tsopouridis and Ioannis Fudos

This paper surveys the state of transparency rendering in Computer-Aided Design (CAD), with a focus on both practical deployment in industrial systems and the underlying algorithms. We first review current approaches to transparency rendering in CAD environments and outline application scenarios in which accurate and performant transparency is critical for design inspection, communication, and decision-making. We then analyze the trade-offs between approximate and exact transparency techniques, comparing their performance-quality balance on desktop and mobile platforms and discussing criteria for selecting appropriate methods. The survey further identifies the need for robust benchmarks, quality metrics, and evaluation methodologies tailored to CAD-specific visualization tasks. We examine techniques for emphasizing important interior components such as importance-driven transparency, silhouette-based methods, and related approaches to support effective spatial understanding in complex assemblies. Finally, we discuss the unique challenges of rendering transparent constructive solid geometry (CSG) objects, including robustness, correctness, and integration with modern rendering pipelines. Collectively, these contributions characterize current capabilities, systematize open problems, and outline future research directions for transparency rendering in CAD visualization.
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Graphics

What Will This Copper Look Like Later? Forecasting Surface Appearance and Rendering It as a PBR Material

Teejuta Sriwaranon, Borworntat Dendumrongkul, Tanapat Chamted et al. (+1 more)

Digital design requires predicting how a metal surface will look later in its oxidation; this paper presents such a pipeline for copper. Given a fixed-camera observation, the system forecasts appearance 10 accelerated units ahead and converts it into the albedo, normal, roughness and metallic maps a renderer consumes. Forecasting is evaluated as an authoring tool would use it, on a copper specimen the system has not observed: an entire recording is held out, so training and checkpoint selection use one specimen and the test set is the whole of a second, recorded on a different day and condition. Under this protocol a learned spatio-temporal model with a monotone oxidation state, the most accurate forecaster within a single recording, is less accurate than copying the last observed frame on an unseen specimen, in both directions, as are three further trained architectures. The only forecaster that transfers is a closed-form global color extrapolation with no trained parameters, improving on copy-last-frame by 13.4% and 50.6%, with a margin that increases with horizon to +16.7% and +55.5% at t+10. Two controls qualify this: correcting every frame for the photometric drift measured on a non-oxidizing reference region leaves both margins intact, ruling out uncontrolled exposure as their source, and a moving-block bootstrap over the 6 independent windows each recording contains separates the larger margin from zero but leaves the smaller one not individually significant. The mechanism is measured: a learned susceptibility map encodes where corrosion begins on the training specimen and misleads on a new one, whereas the global color trajectory is what specimens share. The pipeline therefore deploys the closed-form forecaster for unseen specimens and the learned model only for continuing one already observed. Code, splits, protocol and leakage audit are released.
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All authors Teejuta Sriwaranon, Borworntat Dendumrongkul, Tanapat Chamted, Pizzanu Kanongchaiyos

Graphics · Cross-listed from cs.CV

Thread-Efficient Decoding for Neural Texture Compression

Janarbek Matai, Sho Ikeda, Lukasz Lipski et al. (+1 more)

Neural texture compression (NTC) achieves higher compression ratios than BCn formats but suffers from GPU thread divergence, which significantly reduces runtime performance. In this work, we propose a shared decoder MLP architecture – trained with a gradual decoder freezing schedule – combined with texture clustering to reduce thread divergence by 25%-52% while preserving rendering quality. We evaluate our method on over 500 textures and multiple real rendering scenes, demonstrating up to 8.48x speedup on the Radeon RX 9070 XT GPU compared to non-shared baselines. Our key contributions include: (1) a unified shared decoder architecture that reduces divergence by grouping textures; (2) a training recipe with gradual decoder freezing that improves stability and reconstruction accuracy; (3) a semantic clustering strategy using CLIP embeddings that groups similar textures for effective decoder sharing; and (4) comprehensive performance and ablation studies validating our approach.
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All authors Janarbek Matai, Sho Ikeda, Lukasz Lipski, Takahiro Harada

Thursday 27 August

Graphics · Cross-listed from cs.CV

ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields

Ka Heng Shiu and Kartic Subr

We present ABCD (Alpha-Composited Block Coordinate Descent), an out-of-core training framework for alpha-composited radiance fields, instantiated here for 3D Gaussian Splatting. Our method reformulates training as block coordinate descent over spatial partitions: only one block of parameters is active at a time, while all others are frozen. By exploiting the associativity of alpha blending, these inactive regions can be pre-rendered and collapsed into foreground and background RGBA images. As a result, for fixed partition size and image resolution, peak VRAM becomes O(1) with respect to total scene extent, rather than growing with full scene size. This enables GPUs with limited memory to train scenes that would otherwise not fit in core. In experiments, our method closely preserves the reconstruction quality of 3DGS, with less than 5% PSNR degradation, while ABCD with compositing ablated suffers roughly 40% degradation. Our code can be found at https://github.com/shiukaheng/abcd
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Graphics

The Role of Mixed and Augmented Reality in Medical Visualization: Literature Review and A Context-Aware Taxonomy

Xinrui Zou, Mingxu Liu, Hongchao Shu et al. (+3 more)

The discovery and evolution of medical imaging technologies have enabled non-invasive visualization of internal anatomy that has be- come essential for supporting diagnosis, monitoring, and treatment. However, because medical imaging relies on complex physical processes and contrast mechanisms for image formation, imaging alone is not sufficient to enable humans to leverage the resulting in- formation fully. In addition, traditional methods to visualize the resulting information use two-dimensional displays to present three-dimensional anatomical structures. The introduction of Augmented and Mixed Reality (AR/MR) technologies offers an opportunity to provide valuable paradigms for medical imaging visualization, allowing users to observe, explore, and interact with anatomical infor- mation in more spatially intuitive ways. However, naive implementation without careful design considerations can lead to perceptual inconsistencies, potentially compromising utility and effectiveness. In this paper, we present a structured taxonomy of medical AR/MR visualization strategies aimed at providing clearer insight into how visualization design varies across clinical use cases. The taxonomy organizes techniques based on four core design components: image modality, data dimensionality, display technology, and clinical application. In addition, we introduce two critical dimensions that are often overlooked in the literature: visualization anchoring (the spatial relationship between virtual content and the physical world), and perceptual awareness (the use of visual cues to support spatial interpretation). Together, these components form a comprehensive taxonomy, offering a detailed framework for selecting appropriate visualization techniques in medical applications.
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All authors Xinrui Zou, Mingxu Liu, Hongchao Shu, Ruixing Liang, Mathias Unberath, Alejandro Martin-Gomez

Graphics

Visual Cue Interactions in AR-Guided Needle Insertion: A Prostate Biopsy-Inspired Phantom Study

Xinrui Zou, Mingxu Liu, Thomas T. Jones et al. (+5 more)

Despite the apparent simplicity of the motor action involved during percutaneous needle procedures, manipulating the tool's direction becomes challenging when clinicians cannot directly visualize internal anatomy and must rely on ultrasound images, which increase cognitive demand. Augmented reality (AR) offers the promise to assist with these tasks by providing pertinent visual information in the clinician's field of view. However, simply providing visual information that ignores meaningful visual cues can complicate depth perception and spatial understanding. In this work, we introduce and evaluate three visualization techniques for needle alignment developed during design sessions with medical experts: a localized focus-and-context window, a color-based proximity encoding, and an explicit trajectory overlay. These techniques were evaluated in a user study (n=26) including clinical experts (n=7) using a prostate-biopsy-inspired phantom. Results from this study suggest that cue effects depended on the surrounding cue configuration and user expertise. For novices, explicit trajectory overlay improved targeting accuracy and reduced retreat behavior, but its effect on completion time varied across cue configurations, with slower performance when the overlay was presented alone. For experts, the focus-and-context window reduced completion time and retreat events, while color-based proximity overlay improved completion time. Subjectively, color cues were often perceived as helpful even when their effects on accuracy were not consistent. These results suggest that AR guidance strategies for percutaneous interventions should consider user expertise and visual context.
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All authors Xinrui Zou, Mingxu Liu, Thomas T. Jones, Braden Millan, Sandeep Gurram, Peter A. Pinto, Raisa Z. Freidlin, Alejandro Martin-Gomez

Graphics

Comparative Evaluation of 3D Reconstruction Methods for Immersive Visualization of Laboratory Objects

Brian De La Cruz, Aaron Y. Zhao, Maitrey Gramopadhye et al. (+4 more)

In this study, we examined whether current 3D reconstruction methods can support the creation of realistic holographic representations of laboratory objects for educational use. In this regard, we compared four approaches: photogrammetry, a neural radiance field (NeRF)-based method, Gaussian splatting, and LiDAR. These methods were used to generate holographic models of common laboratory items and their fidelity was evaluated by graduate students. Participants assessed the models for shape, color, texture, and visual defects using a repeated-measures design. Across objects, the NeRF-based method produced the most consistently high-fidelity representations, particularly for transparent, reflective, or low-texture items that were difficult to capture with other approaches. Shape and color were generally reproduced more successfully than texture, suggesting that some visual properties remain more challenging to represent accurately in educational holograms. Beyond identifying the strengths and limitations of each reconstruction method, the study demonstrates a practical workflow for creating immersive learning objects that may support pre-laboratory preparation, spatial reasoning, and student engagement in AR/MR-based educational environments. These findings offer design-relevant insights for educators and researchers developing immersive digital learning experiences.
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All authors Brian De La Cruz, Aaron Y. Zhao, Maitrey Gramopadhye, Sawyer J. Lazar, Xianming Tan, Daniel Szafir, David S. Lawrence

Graphics · Cross-listed from cs.HC

Surrounded by Friends: Design and Evaluation of Immersive Layouts of Egocentric Network for Visual Analytics

Kentaro Takahira, Takanori Fujiwara, Wong Kam-Kwai et al. (+5 more)

This paper explores design considerations for egocentric network layouts in immersive environments, providing fresh empirical insights that enhance egocentric network analysis. An egocentric network focuses on the topological and semantic relationships around a focal node (ego) and its neighboring nodes (alters), targeting local sub-networks rather than the whole network. Traditional desktop environments, limited by display constraints, often face visual clutter as node numbers grow. Building on recent findings that immersive environments enhance network analysis, we explore layouts tailored for these spaces. We begin by identifying essential design properties and dimensions for egocentric network layouts, taking into account the unique features of immersive environments. Based on these, we design four layouts-Cube, Cylindrical, Radial, and Spherical-that vary across design dimensions. We evaluate these layouts in a user study with 24 participants completing egocentric analysis tasks. Our study suggests that Cube performed well for tasks focused on ego-alter connection strength. In contrast, Spherical was more effective for understanding alter topology, minimizing occlusion, and efficiently utilizing 3D space. These findings inform design implications for future immersive egocentric network layouts.
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All authors Kentaro Takahira, Takanori Fujiwara, Wong Kam-Kwai, Kento Shigyo, Leni Yang, Hiroaki Natsukawa, Yalong Yang, Huamin Qu

Graphics · Cross-listed from cs.HC

Dynamic Tree Colors: Adaptive Discriminable Hierarchies with Minimum Instability

Tobias Mertz, Steven Lamarr Reynolds and Jörn Kohlhammer

Hierarchical color maps can support users in the analysis of hierarchical data. For large hierarchies, dynamic color maps can improve discriminability upon user interactions, but the incremental color changes may cause users to lose their orientation in the data set. To address this challenge, we present Dynamic Tree Colors, a dynamic hierarchical color map that can be configured to a suitable tradeoff between discriminability and color stability. We also define quality metrics for both criteria and investigate our algorithm's performance with respect to these metrics as well as a user study with 18 participants. Our results indicate that Dynamic Tree Colors yields good results in a wide range of application scenarios, but it does not achieve the performance of the state-of-the-art algorithm Cuttlefish in the specific scenario that algorithm was designed for.
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Wednesday 26 August

Graphics · Cross-listed from cs.CV

Evaluating Constrained Iterative Refinement for Scalable Vector Graphics Generation with Off-the-Shelf VLMs

Matthew Perlman, James Beetham, Niels Da Vitoria Lobo et al. (+2 more)

Scalable Vector Graphics (SVGs) power much of the modern visual ecosystem, yet state-of-the-art generative models focus almost entirely on rasterized images. We explore whether inference-time methods can unlock SVG generation capabilities in off-the-shelf vision-language models (VLMs). We systematically evaluate a constrained iterative refinement harness that combines visual feedback, structured editing, and constrained decoding to characterize the capabilities and limitations of current VLMs for SVG generation. Across multiple VLMs and generation settings, we find that constrained decoding improves compilation success rates, while iterative refinement reveals a deficit in visual reasoning and self-correction. Our results highlight both the promise and current limitations of using inference-time methods to adapt general-purpose VLMs for SVG generation.
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All authors Matthew Perlman, James Beetham, Niels Da Vitoria Lobo, Amrit Singh Bedi, Mubarak Shah

Graphics · Cross-listed from cs.CV

Procedura: Agentic 3D Modeling with Procedural Control

Youtian Lin, Yikang Yang, Zhanpeng Hu et al. (+5 more)

Native 3D generators now recover impressive mesh geometry from a single image. However, a dense mesh stays soft where a machined object should be sharp, it carries no part decomposition, and it exposes no parameter a user could edit. To address this, we explore the paradigm of 3D shape as code, leveraging and scaling the coding ability of an LLM for 3D modeling. We introduce Procedura, a novel 3D modeling agent framework that writes an object as a procedural assembly, a parametric program whose named parts are joined by typed, machine-checkable mates. From a text prompt, the agent plans the object as an assembly graph and writes the program part by part, solving each placement from the mated frames rather than guessing it, and admitting a part only once compile, mate, and connectivity checks pass. A decoupled vision critic then refines the assembly one diagnosed fix at a time. Moreover, the same graph carries per-part materials and a simulator-validated articulation. We evaluate on P3D-Bench under its assembly judge, and with the same judge on MechBench-36, our hard-surface benchmark. On both, Procedura outperforms state-of-the-art native 3D generators and every prior 3D-code agent on judged quality, produces the sharpest edges of any method we evaluate, and is the only one whose output is an editable, part-structured program.
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All authors Youtian Lin, Yikang Yang, Zhanpeng Hu, Mengqi Zhou, Feihu Zhang, Xun Cao, Jiaheng Liu, Yao Yao

Graphics · Cross-listed from cs.HC

TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking

Yuexin Sun, Zhaohui Wang, Ruiyang Liu et al. (+4 more)

Experience-driven manufacturing, such as garment pattern making, faces a severe generational skills gap because its core expertise relies on undocumented tacit knowledge forged through day-to-day practice. To address this challenge, we present TailorCoPilot, an agentic pattern-making system built upon a specially designed version-control backend TailorTrace. TailorTrace models sewing patterns as structured, discrete states and records their transformations during the pattern-making process as explicit operation sequences defined upon the geometry primitives in the sewing pattern (panels, edges, vertices and stitches). Integrated into a conventional pattern-making GUI, TailorTrace enables seamless documentation of senior experts' tacit pattern-making knowledge without breaking their daily workflow. The documented knowledge further offers interactive, pedagogical scaffolding for novices, while providing a robust foundation to power TailorCoPilot and train future generative AI models. In a user study with novices and advanced novices, TailorCoPilot improved task completion rates, reduced time and perceived workload, and yielded higher-quality artifacts compared to skill-appropriate baselines. Ultimately, TailorCoPilot demonstrates a viable pathway to capture practice-based expertise, operationalizing it to support both generative AI advancements and human apprenticeship.
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All authors Yuexin Sun, Zhaohui Wang, Ruiyang Liu, Demian Kong, Qian He, Gaofeng He, Huamin Wang

Graphics

GLOSS: Geometric Local Self-Similarity Learning for Faithful Reference-Guided Texture Fill

Chenyue Cai, Anita Hu, James Lucas et al. (+2 more)

Using conditional image generators, texture artists can explore many single-view looks for an existing 3D shape. Despite impressive progress, state-of-the-art generative methods still struggle to generate a full object texture while closely adhering to fine scale geometric detail and single view references, leaving little room for artists guidance. Furthermore, current automatic models lack the flexibility for artist to explore multiple textures from varied sources in an interactive and controllable manner. Unlike methods trained on large 3D datasets that generate full object textures from global guidance, our work explores a local and less data-hungry approach to texture with explicit artist control. We leverage the geometric self-similarity and geometry-texture correlation existing in many natural and man-made shapes; and train a shape-specific local texture generation and completion model. This model learns from existing image model priors and a single 3D shape, and is guided by attending to a set of geometry-aware reference patches. The trained shape-specific network can transfer any novel reference to the full target object texture through patchwise inpainting. We show improved or comparable quality to strong image-conditioned texture generation baselines, suggesting local texturing as a promising research direction. Our model also enables local geometry-conditioned texture inpainting, guided by artist-selected references, and generalizes to PBR materials and unseen meshes for texture transfer. We piloted our novel texture fill capability as a Blender addon with several 3D texturing professionals who reported positive feedback on the model's controllability, practical usefulness, and creative affordances.
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All authors Chenyue Cai, Anita Hu, James Lucas, Szymon Rusinkiewicz, Masha Shugrina

Tuesday 25 August

Graphics

Hamiltonian Two-Way Coupling of Nonlinear Waves and 3D Flows

Sinan Wang, Ruicheng Wang, Taiyuan Zhang et al. (+5 more)

Simulating large-scale free-surface water by coupling a localized 3D fluid solver to a cheaper 2D surface model has long faced a mismatch in wave dynamics: efficient 2D wave models used in graphics are typically either linear or non-dispersive. These models are fast, simple, and accurate for calm, small-amplitude seas, but coupling them with strongly nonlinear 3D solvers produces visible reflections and artifacts at the 2D–3D interface. We address this problem by introducing a nonlinear and dispersive 2D wave model based on the canonical Zakharov formulation. Its Hamiltonian structure, in which the surface elevation and surface potential form a canonical pair ($\eta$, $\psi$) governed by the wave energy, enables a canonically consistent two-way coupling scheme, allowing information to pass smoothly across the 2D–3D interface. Our 2D solver reduces mean wave-height error by 1.7–5$\times$ over SWE, BEM, and Airy baselines while running more than $10^3\times$ faster than BEM; it achieves greater nonlinear accuracy and coupling fidelity than SWE and Airy, with minor losses in speed and stability. Coupling it with a 3D Navier–Stokes solver yields a full system that suppresses visible seam artifacts across a range of experiments, including dispersion-matching and Kelvin-wake tests, and runs over 4$\times$ faster than a pure GPU NB-FLIP simulation on the same domain.
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All authors Sinan Wang, Ruicheng Wang, Taiyuan Zhang, Fan Feng, Jinjin He, Yuchen Sun, Zhiqi Li, Bo Zhu

Graphics · Cross-listed from cs.CV

SeMoCo: A Semantic-First Motion Codec for Motion Language Modeling

Tianlv Huang, Hetian Guo, Ziyi Cai et al. (+6 more)

Discrete motion representations have substantially advanced autoregressive text-to-motion generation. However, most motion tokenizers are optimized for reconstruction and do not explicitly allocate capacity according to semantic role. Action-level meaning and fine-grained kinematic detail must therefore be encoded through the same reconstruction-driven hierarchy. We introduce SeMoCo, a semantic-first motion codec, together with a dual-axis motion generator for language-conditioned motion generation. Each motion token contains one semantic token and a residual sequence of kinematic tokens. The generator models semantic progression across time and autoregressively refines the residual entries. We also construct $\Omega$-MotionVerse, a large-scale, multi-source human-motion dataset unified under the SOMA representation. Across the reported comparisons, SeMoCo achieves the best reconstruction accuracy among the compared codecs, while strong text-to-motion results demonstrate the effectiveness of its motion tokens for downstream generation.
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All authors Tianlv Huang, Hetian Guo, Ziyi Cai, Song Wang, Yanping Zhang, Zipei Fan, Xuan Song, Guangming Wu, Xin Zheng

Graphics · Cross-listed from cs.CV

ViSculpt: Visual-Centric Agentic Geometry Editing

Bo Pang, Jiaqi Pan, Xiaocheng Zhang et al. (+3 more)

3D geometry editing is a critical yet labor-intensive part of the graphics pipeline, requiring artists to translate creative intent into precise operations in complex professional software. Large language models (LLMs) have shown promise for script-based 3D creation, but script generation is less suited to perception-driven editing of arbitrary existing meshes, where execution must remain visually grounded and untouched regions should be preserved. We present a visual-centric, training-free multi-agent system that edits existing 3D meshes directly in Blender by emulating the iterative workflow of human artists. Rather than generating scripts or regenerating geometry, our system operates through the Blender GUI: multimodal LLM agents observe the viewport, reason about the current mesh state, and execute localized edits through simulated user interactions. Experiments on a curated benchmark provide initial evidence that this agentic approach can follow natural language instructions, perform representative localized mesh edits, and preserve the overall identity of the input asset. Our results highlight a complementary regime for language-driven 3D editing: direct in-place modification of existing meshes within the native 3D editing workflow. We view this work as an exploratory step toward visual-centric agentic geometry editing in professional graphics software.
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All authors Bo Pang, Jiaqi Pan, Xiaocheng Zhang, Jiacheng Xu, Guoping Wang, Peng-Shuai Wang

Graphics

ExMesh++: From Multi-View Images to Relightable UV-PBR Mesh Assets via Topology-Adaptive Reconstruction and Decomposition

Chuanjin Fan, Lifan Wu, Wenjie Chang et al. (+3 more)

Multi-view reconstruction extends beyond surface recovery to editable and relightable mesh assets. Such assets require well-formed topology, valid UV parameterization, and explicit PBR material maps. Existing surface reconstruction approaches optimize implicit fields, Gaussian primitives, or other intermediate representations. Converting them into such assets often requires surface extraction and texture baking. Inverse-rendering methods estimate materials and illumination, yet these components often remain tied to neural fields or point-based primitives rather than the final mesh. Joint optimization of geometry, materials, and lighting may also allow these variables to compensate for one another, leading to ambiguous decomposition. To address these limitations, we present ExMesh++, a staged framework for reconstructing relightable UV-PBR mesh assets from multi-view images. The first stage refines explicit mesh geometry and topology through adaptive vertex splitting and merging, while maintaining UV consistency as the topology changes. The second stage fixes the resulting mesh-UV carrier and optimizes UV-space PBR maps together with environment lighting. Building on this stable carrier, ExMesh++ models one-bounce diffuse indirect illumination through secondary-ray tracing with shared UV-PBR materials. Experiments demonstrate competitive geometry accuracy, strong relighting performance, and direct usability of the exported assets in standard DCC workflows.
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All authors Chuanjin Fan, Lifan Wu, Wenjie Chang, Hanzhi Chang, Wenfei Yang, Tianzhu Zhang

Graphics · Cross-listed from cs.CV

Luce: Relightable Gaussians for 3D Asset Generation

Mayank Singh, Michele Stoppa, Alvise Memo et al. (+7 more)

High-fidelity image-to-3D generation requires a 3D representation that captures both geometry and appearance. However, preserving fine detail across the physically based rendering (PBR) modalities needed for relighting remains challenging. To address this, we propose Luce, a 3D representation that unifies geometry and PBR materials within a voxelized multimodal Gaussian cloud, using dedicated Gaussian primitives for albedo, metallic-roughness, and surface normals. A variational autoencoder compresses this representation into a unified material-aware latent space. A rectified-flow transformer generates this latent from a single image using multi-layer features from a pretrained image encoder that preserve both semantic context and fine spatial detail. The latent is then decoded into relightable PBR Gaussians and an optional textured mesh with a tangent-space normal map. On Toys4K, Luce achieves state-of-the-art single-image-to-3D generation, improving FID by 28% over the strongest baseline. We further evaluate Luce on a benchmark of AI-generated images depicting diverse subjects and materials, where it improves the CLIP image-alignment score over the best baseline (0.8519 vs. 0.8299). Luce generates relightable, geometrically accurate, and materially faithful assets that preserve fine details such as text, logos, and inscriptions.
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All authors Mayank Singh, Michele Stoppa, Alvise Memo, Rui Yu, Sree Harsha Kalli, Srimanth Gunturi, Muhammad Ahmed Riaz, Behrooz Shahsavari, Waleed Abdulla, David E. Jacobs

Monday 24 August

Graphics · Cross-listed from cond-mat.soft

Electrospun Fields: 3D Nano-Fiber Material Computation as Design Method

Wai Lok Wan, Ayah Mahmoud, Sergio Mutis et al. (+3 more)

We present a robotic electrospinning platform and design method for depositing nanofiber membranes onto non-planar, three-dimensional conductive geometries. Conventional electrospinning relies on fixed emitters and planar grounded collectors, which restricts deposition to flat substrates: on concave geometries, field shielding prevents fibers from reaching recessed regions, and material bridges across elevated features instead. We address this with a custom end-effector integrated with a six-axis UR20 arm. The tool carries a localized stepper-driven syringe pump that maintains consistent polymer flow independent of orientation, and routes high-voltage DC (up to 25 kV) directly to a robot-mounted needle, turning the arm into a mobile emitter with full kinematic control over position, orientation, working distance, and traversal velocity. Toolpaths that continuously reorient the emitter along surface normals give access to concave topologies unreachable by fixed-axis systems. We characterize the resulting deposition behavior in two parts. A catalog of four bio-compatible polymer systems (PEO, PVA, keratin-PEO, silk-PEO) establishes the operating envelope, reporting deposition speed, jet stability, fiber size, alignment, and durability for each. A taxonomy of 3D-printed conductive scaffolds spanning geometric primitives, hybrid compositions, and square, triangular, and hexagonal lattices links collector geometry to fiber alignment, density, and cross-void bridging. We also demonstrate programmable grounding, in which selectively energized pins in an array steer deposition without changing physical geometry. Assembly instructions and toolpath-generation code are released as an open-source repository.
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All authors Wai Lok Wan, Ayah Mahmoud, Sergio Mutis, Avantika Velho, Annie Xing, Behnaz Farahi

Sunday 23 August

Graphics

A Three-Parameter Binary Subdivision Scheme for Shape-Controlled Curve Design

Rabia Hameed, Jihad Younis, Maryam Salem Alatawi et al. (+2 more)

Shape-controlled curve design plays a fundamental role in computer-aided geometric design, computer graphics, and engineering applications. In this paper, we present a novel three-parameter 9-point binary approximating subdivision scheme constructed by a weighted combination of the refinement rules of the 7-point Lagrange and 7-point B-spline subdivision schemes. The proposed construction employs displacement vectors between the corresponding refinement points of the parent schemes and constructs resultant vectors by combining neighbouring displacement vectors through three independent design-control parameters. These resultant vectors are subsequently used to derive the refinement rules, thereby yielding a unified family of binary subdivision schemes with adjustable geometric characteristics while preserving the approximating nature of the refinement process. Two representative sub-schemes are derived as special cases by imposing suitable constraints on the design-control parameters. Theoretical investigations establish the support, continuity, endpoint rules for open polygons, and Gibbs oscillation behavior of the proposed family, while graphical examples demonstrate the influence of the design-control parameters on the generated limit curves. Owing to its flexible geometric construction, intuitive parameterization, and favorable mathematical properties, the proposed subdivision framework provides a flexible and effective framework for design-controlled curve design and a valuable tool for computer-aided geometric design and related engineering applications.
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All authors Rabia Hameed, Jihad Younis, Maryam Salem Alatawi, Tahreem Farman, Hafiza Sana Mukhtiar

Saturday 22 August

Graphics · Cross-listed from cs.CV

Emotion Intensity Matters: Generating Realistic Expressions in Virtual Humans with CVAEs

Vitor Miguel Xavier Peres, Lara Volpato, Gabriel Ferri Scnheider et al. (+1 more)

Generating expressive facial behavior in virtual humans (VHs) remains a central challenge in affective computing and character animation. This paper presents a novel approach based on Conditional Variational Autoencoders (CVAEs), trained on real human facial expression data, to synthesize controllable emotional expressions at varying intensities. Using a dataset comprising six basic emotions represented at two intensity levels (low and high), we train a CVAE model to generate synthetic facial expression data while preserving semantic consistency with real human expressions. Despite the limited amount of training data (only 7,680 facial expression samples), the proposed approach learns meaningful latent representations and generates coherent emotional variations. Our method enables control over emotional intensity, making it suitable for animating virtual characters without requiring actor performances or manual artistic intervention. Our research aimed to evaluate whether the method (CVAE) preserves the characteristics associated with the different intensity levels present in the dataset. Results show that the proposed model preserves key expressive characteristics across intensity levels while supporting generalization across emotional intensity levels, contributing to the creation of emotionally expressive virtual characters from relatively small datasets.
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All authors Vitor Miguel Xavier Peres, Lara Volpato, Gabriel Ferri Scnheider, Soraia Raupp Musse

Friday 21 August

Graphics

Orientation in Extended Position-Based Dynamics: Application to Rigid Bodies and Cosserat Rods

Samuel Tobin and Caleb Rucker

Rotational degrees of freedom in Extended Position-Based Dynamics (XPBD) require computations on the nonlinear manifold of 3D rotations. We show that Lie theory provides a clean, unified framework for expressing rotations, constraints, interpolation, and differentiation in XPBD, enabling both improved rigid-body constraints and higher-order finite-element Cosserat rods. We derive explicit Lie-theoretic constraint formulations and their gradients for rigid-body simulation, improving the dynamic consistency of constrained rigid-body simulations in XPBD by a factor of over $10^4$ compared to the state-of-the-art. Our framework naturally extends to finite-element Cosserat rods by enabling on-manifold interpolation of nodal rotations. Linear finite elements outperform the conventional chain-of-rigid-bodies discretization, while higher-order basis functions provide even smoother solutions and faster convergence. Utility is demonstrated in a variety of examples with large deformations and contact.
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Graphics · Cross-listed from cs.MM

When Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception

Guangyuan Dong, Chuang Liu, Haoyu Wang et al. (+10 more)

Multimodal information systems increasingly route generated visual content back through the same vision-language index that informed its production, so the output must remain retrievable by the queries it was meant to serve. When the scene contains entities at vastly different scales, existing language-guided generators condition on a single, globally pooled text embedding and quietly drop scale-specific concepts, breaking concept-query retrieval even when pixel fidelity is high. We formalise this failure as semantic collapse and propose CERES, a closed-loop multimodal indexing framework that builds a three-level semantic pyramid, mines implicit concepts via a co-occurrence-aware router, performs scale-routed cross-attention into a lightweight U-Net generator, and verifies coverage by re-indexing the generated image with the same frozen VLM. A continuously differentiable soft-Jaccard coverage objective returns dense gradients to the 0.39 M-parameter generator under explicit non-degeneracy conditions, and coverage is verified by an independent DINOv2 linear probe trained only on external scene and object labels. On four pansharpening benchmarks across seven settings, CERES delivers the new state of the art with the largest gains where scale variation is most extreme (+4.64% relative Q2n and +9.7 mAP for DOTA detection). It also improves concept-query retrieval Recall@5 by +14.0 points and image-text mean reciprocal rank by 0.19 over the strongest baseline, showing that the closed loop preserves queryable content rather than self-referential feature consistency.
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All authors Guangyuan Dong, Chuang Liu, Haoyu Wang, Yangchen Zeng, Jiaqi Zhang, Li Jiuxing, Xiaoyang Yu, Pinlong Zhao, Yuchao Hou, Ziwei Li, Zheng Lin, Alexander Lim Han Yang, Yusen Wu

Graphics

CubicSplat: Differentiable Vector Graphics via Error-Bounded Forward Relaxation

Chenglong Liu, Xin Zhang, Yimeng Zhu et al. (+5 more)

Vector graphics are prized for their resolution independence, compact storage, and direct editability, making differentiable optimization of their parametric primitives an attractive goal. Yet classical rasterization is discontinuous with respect to geometry, and existing remedies that smooth the forward pass demand increasingly elaborate heuristics as scene complexity grows. We trace this fragility to a gradient seesaw: design choices that improve forward geometric exactness can systematically degrade the induced gradient signal, and vice versa. To navigate this tension we introduce CubicSplat, a differentiable vector rasterizer that replaces Bézier closest-point solvers with uniform polyline surrogates whose geometric error is bounded at $O(S^{-2})$. The resulting static computation graph yields well-conditioned gradients by construction, while a compositing-derived visibility mechanism prunes degenerate primitives without auxiliary regularization. On DIV2K and Kodak benchmarks CubicSplat achieves state-of-the-art reconstruction quality with over 2 dB PSNR gain in the closed-fill setting, while training up to 4x faster than prior methods. The code is available at https://github.com/CubicSplat/repo
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All authors Chenglong Liu, Xin Zhang, Yimeng Zhu, Liyang He, Yixiao Ma, Yu Su, Zhenya Huang, Qi Liu