Computer Vision
Trinh Tra Giang Nguyen, Thanh Nguyen Vo, Nguyen Hoai Thuong Bui et al. (+1 more)
Accurate traffic
forecasting requires both understanding scene dynamics and synthesizing realistic future observations. Recent
diffusion-based
video generation models produce visually plausible predictions but require expensive end-to-end training and often entangle scene understanding with image synthesis. In this work, we propose a decoupled
forecasting framework that separates future representation learning from
video generation. A frozen V-JEPA encoder first extracts predictive latent representations from the observed traffic
videos, capturing the underlying scene dynamics in a semantic latent space. A lightweight latent
alignment module then projects these representations into the conditioning space of a frozen Cosmos
diffusion module, enabling future
video synthesis without retraining the large generative model. By freezing all foundation models and training only the lightweight
alignment module, the proposed framework substantially reduces optimization complexity while preserving
forecasting capability. Experimental results on the AI City Challenge 2026 Track 5
benchmark demonstrate that the proposed method achieved a score of 75.1297, ranking third in the competition. These results suggest that predictive world representations learned by V-JEPA can effectively guide downstream
video generation, providing a practical and efficient alternative to end-to-end
diffusion-based
forecasting.
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All authors Trinh Tra Giang Nguyen, Thanh Nguyen Vo, Nguyen Hoai Thuong Bui, Ha Duc Bui
Computer Vision
RobotEQ-Video: A Video-Centric Benchmark for Social Proactive Intelligence with World-State Taxonomy
Xinyi Che, Zheng Lian, Kuofei Fang et al. (+13 more)
Social Proactive Intelligence (SPI) extends proactive assistance beyond task completeness to consider social appropriateness in diverse
embodied scenarios. However, prior SPI research faces two key limitations. First, existing work focuses on static images, whereas dynamic
videos provide crucial cues for inferring human states and needs, offering richer information than isolated images. Second, prior work often relies on free-form data collection pipelines, which fail to guarantee comprehensive coverage of diverse scenarios. To address these gaps, we introduce RobotEQ-
Video, shifting the focus from image-centric to
video-centric analysis. To ensure comprehensive
video coverage, we construct a hierarchical world-state taxonomy organized into a four-level coarse-to-fine structure, comprising 6 domains, 20 dimensions, 142 level-1 attributes, and 816 level-2 attributes. The resulting
benchmark comprises 2K+
videos with 100K+ human annotations and 16K+ labels for assessing behavior properness.
Benchmark evaluation reveals that current systems remain unreliable and fall short of human performance. We further explore how world models can help tackle this task. This work advances SPI research from static images to dynamic
videos and ensures more comprehensive scenario coverage during benchmarking.
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All authors Xinyi Che, Zheng Lian, Kuofei Fang, Xuehao Wang, Xinghai Gao, Junqing Wu, Chuyu Wu, Liyi Liu, Yanhan Huang, Keyi Xie, Haomin Ouyang, Jinyang Wu, Fan Zhang, Runhao Zeng, Xun Yang, Bin He
Computer Vision · Cross-listed from cs.RO
ProTracer: Proprioception-Guided Failure Diagnosis in Robot Manipulation
Chang Dong, Mehdi Hosseinzadeh, King Hang Wong et al. (+4 more)
This paper presents a comprehensive framework for
robot manipulation failure analysis that includes binary failure detection, failure categorization, explanation generation, and the additional capability of failure onset localization, which aims to identify the earliest moment at which a
robot execution deviates from a valid task-completion trajectory and is ultimately followed by task failure. To address these tasks, we propose ProTracer, a training-free framework that leverages existing
Vision-
Language Models (VLMs) together with proprioceptive signals for failure analysis. Our method uses proprioceptive dynamics to identify temporally informative action boundaries and converts richer
robot-state signals into structured natural-
language descriptions that can be jointly analyzed together with visual observations by the VLM. This design combines the temporal precision of proprioceptive signals with the
multimodal reasoning capabilities of modern VLMs without requiring additional model training. We further introduce FailTime, a
benchmark with synchronized visual and proprioceptive observations for evaluating conventional failure diagnosis tasks as well as failure onset localization. Experiments demonstrate that ProTracer achieves strong performance across both conventional failure diagnosis tasks and the newly introduced failure onset localization task, highlighting the importance of proprioceptive
reasoning for fine-grained temporal failure analysis.
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All authors Chang Dong, Mehdi Hosseinzadeh, King Hang Wong, Lingqiao Liu, Francois Fraysse, Feras Dayoub, Minh Hoai
Computer Vision
Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models
Yining Wang, Xi Li, Mi Zhang et al. (+4 more)
Multimodal large
reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introduces a critical yet underexplored
privacy threat: adversaries can exploit MLRMs to precisely infer users' geographic locations from casually shared photographs, by performing structured
reasoning over subtle visual cues such as architectural styles, vegetation, and lighting conditions. In this work, we present a systematic study of MLRM-driven geolocation
privacy leakage. We first reveal that refusal-based safeguards are critically insufficient, as carefully crafted jailbreak
prompts can raise model response rates to 100%. We further identify that existing defenses, which inject imperceptible perturbations into shared images, suffer from structural limitations intrinsic to their pixel-space optimization, resulting in degraded black-box transferability and pronounced visual artifacts. Motivated by these findings, we propose a
diffusion-based framework that provides targeted, proactive defense against geolocation
privacy leakage. By injecting perturbations into the latent space of a
diffusion model during reverse sampling, our method operates directly on high-level semantic representations, thereby resolving the effectiveness-utility bottlenecks by construction. We further ground our optimization with GeoCLIP, a model explicitly aligned with GPS coordinates, as a surrogate to pinpoint and disrupt the geographic signals that MLRMs exploit for location inference. This targeted semantic disruption yields significantly stronger black-box transferability while preserving perceptual image quality, offering a seamless integration on social media platforms. Code is available at https://github.com/RachelWolowitz/Hiding_in_plain_sight.
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All authors Yining Wang, Xi Li, Mi Zhang, Xiaohan Zhang, Xiaoyu You, Zhenxing Qian, Mi Wen
Computer Vision
Field Tracking of Insects Using a Stereoscopic Event-Based Camera Setup
Pratham G. Shenwai, Martin J. Lankheet, John T. Hrynuk et al. (+3 more)
High-speed tracking of small, fast-moving organisms in their natural environments is important to better understand their behavior and ecology. Traditional frame-based imaging suffers from motion blur due to low temporal resolution, and data storage limitations, propelling a search for more adaptive solutions. Event cameras, which capture changes in brightness at pixel level instead of entire frames, have emerged as a promising solution by increasing temporal resolution and data efficiency. Here, we demonstrate the use of event-based imaging with standard
video-based processing methods by converting the asynchronous events into conventional
video formats, allowing us to leverage the event camera's enhanced temporal detail to capture intricate insect flight movements and apply established image analysis techniques. Coupling this conversion process with a stereoscopic configuration provides continuous, low-latency, three-dimensional tracking of fast-moving subjects in field conditions. As a result, we substantially mitigate motion artifacts and achieve more accurate representations of animal movements. By making event-based imaging more readily applicable in natural field settings, our method support broader applications across animal behavior and ecological research, agricultural management, and other fields requiring high-fidelity object tracking in the wild.
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All authors Pratham G. Shenwai, Martin J. Lankheet, John T. Hrynuk, Mandiyam Y. Mahadeeswara, Mandyam V. Srinivasan, Sridhar Ravi
Computer Vision
PrismAlign: Prior-Steered Multi-View VLM Alignment for Hallucination-Robust Table OCR
Guangyi Liu, Qianjun Huang and Boyu Hou
Table extraction suffers from frequent structural errors and semantic
hallucinations. We propose PrismAlign, a multi-VLM framework aligning diverse visual perspectives to resolve ambiguity. It integrates priors of table logic to assess output plausibility, decoupling structural
alignment from
cell content
alignment. A
Bayesian decision strategy maximizes
alignment accuracy by exploiting the correlation between extraction errors and computable rule violations. Evaluated on open-source and custom VLMs, PrismAlign reduces
hallucinations and achieves state-of-the-art performance on OmniDocBench 1.5, as well as on the table category of CC-OCR and PureDocBench.
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Computer Vision
Cube-Splat: High-Fidelity 360{\deg} Gaussian Splatting SLAM via Cubemap Factorization and Adjoint-Consistent Optimization
Xiangfei Guo, Hao Shi, Yufan Zhang et al. (+4 more)
Recent progress in 3D Gaussian Splatting (3DGS) has enabled dense visual SLAM with pinhole cameras, yet most pipelines are not designed for panoramic imagery. We present Cube-Splat, the first panoramic GS-SLAM framework that factorizes each 360{\deg} frame into a cubemap of four fixed-orientation virtual pinhole views sharing a single optical center. By designating the front face as the primary pose state, we accumulate gradients from all faces via an adjoint mapping, thereby enabling multi-face observations to coherently update a single state while strictly preserving cross-view
geometric consistency. Concurrently, our mapping module densifies and optimizes anisotropic Gaussians using aggregated cubemap rays for high-fidelity, dense reconstruction. Furthermore, to rigorously evaluate panoramic SLAM under diverse and challenging conditions, we introduce SynPano, a highly scalable, photorealistic synthetic
dataset featuring parameterized complex trajectories and multi-modal ground truth. Extensive evaluations on two public
benchmarks (PALVIO and OmniBlender) and our SynPano
dataset, collectively encompassing both indoor and outdoor scenes, demonstrate that Cube-Splat achieves state-of-the-art (SOTA) performance in tracking accuracy and reconstruction fidelity. Both the source code and the SynPano
dataset are available at https://github.com/guoxf304/CubeSplat.
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All authors Xiangfei Guo, Hao Shi, Yufan Zhang, Zhonghua Yi, Yongqi Mao, Xiaoting Yin, Kaiwei Wang
Computer Vision
VeriFuse: Bounded Vision-Language Arbitration and Reason-Guided Refinement for Cooperative 3D Perception
Hongyi Lin, Yiyao Liu, Qi Kang et al. (+4 more)
Vision-
language models (VLMs) have demonstrated strong scene understanding and semantic judgment across diverse tasks, but their appropriate role in cooperative perception remains unclear. Directly asking a VLM to regress 3D detections is unreliable and computationally expensive, whereas using it to select the output of a single source discards useful information from other
agents. We introduce VeriFuse, a bounded arbitration framework for vehicle-infrastructure cooperative 3D detection. Each
agent first produces detections independently. Around each vehicle and roadside proposal, VeriFuse generates source-conditioned
geometric candidates and combines the original detections, their perturbations, and cross-source hypotheses into a unified candidate pool. A frozen VLM then chooses among three admissible actions: SELECT an adequate candidate; REFINE an existing anchor when an object is supported but all candidates are geometrically inadequate; or REJECT an unsupported infrastructure-only proposal. Experiments on the DAIR-V2X
dataset show that VeriFuse achieves 0.494/0.357 cooperative 3D AP50/AP70 and limits the relative vehicle-side BEV AP50 drop under a 300 ms delay to 1.7%. Overall, VeriFuse assigns the VLM a clear and constrained role in cooperative perception: semantic
reasoning resolves ambiguity among cross-
agent hypotheses, while deterministic constraints determine the final 3D geometry.
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All authors Hongyi Lin, Yiyao Liu, Qi Kang, Heye Huang, Yang Liu, Haris Koutsopoulos, Jinhua Zhao
Computer Vision
S3VD: Semantic-Guidance Spatio-Temporal Scanning for Video Deraining
Kui Jiang, Yiang Chen, Yan Luo et al. (+3 more)
Heavy rainfall severely degrades outdoor
videos by corrupting high-frequency details and introducing motion blur, critically undermining the reliability of visual tasks. Recently, State Space Models (SSMs), particularly Mamba, have emerged as efficient alternatives for
vision tasks with their linear complexity and ability to model long-range dependencies. However, when confronted with the poor visual representations in rainy
videos, Mamba still faces difficulties in preserving the integrity of 2D spatial semantics and modeling 3D spatio-temporal correlations. To break these limitations, we introduce S3VD, a Semantic-Guidance Spatio-Temporal Scanning framework for
video deraining, featuring two key innovations: Multi-Scale Semantic Fusion (MSSF) Module and Spatio-Temporal Scanning Fusion (STSF) Module. The former integrates temporal semantic priors from DINOv2 to guide precise feature representation and counteract the loss of local semantic context inherent to Mamba's 1D flatten operation, enhancing robustness against extreme degradation. The latter introduces a spatio-temporal scanning mechanism and devises a Decoupled-Gating Mamba (DG-Mamba) layer, which employs two independent gates to adaptively control preceding and subsequent contextual information within the input clip, optimizing intra-frame and inter-frame correlation modeling. Experiments on
video deraining
benchmarks demonstrate the superiority of S3VD, achieving state-of-the-art performance with an average 0.84 dB PSNR improvement over Mamba-based baselines.
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All authors Kui Jiang, Yiang Chen, Yan Luo, Zhaocheng Yu, Junjun Jiang, Xianming Liu
Computer Vision
Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery
Ik Jae Lee, Hieu D. Nguyen, Mahbubur Meenar et al. (+2 more)
Reliable plant-level information from unmanned aerial vehicle (
UAV) imagery is important for automated crop monitoring. However, in dense crop canopies, adjacent plants frequently overlap and are detected as a single object, reducing the reliability of plant-level measurements. This study presents a geometry-aware post-detection framework for resolving overlapping plant instances using standard RGB
UAV imagery. The framework combines object detection with
geometric clustering of plant components. Leaves or branches detected within each bush-level region are represented using two complementary
geometric features: component centroids and radial intersection points (RIPs) derived from detected plant structures. K-means and Gaussian mixture models determine whether a detected region contains a single plant or two overlapping plants. Density filtering suppresses spurious radial intersections, and a post-pipeline ensemble combines spatial and directional
geometric information. The framework was evaluated using
UAV imagery of eggplant and tomato crops under field conditions. Centroid-based clustering achieved an F1-score of 0.89 for eggplant, while the combined centroid-RIP approach achieved the best tomato performance, with an accuracy of 0.80, precision of 1.00, and F1-score of 0.75 using K-means. Density filtering substantially improved RIP-based clustering for tomato. The proposed approach provides a lightweight, modular engineering solution that can be integrated with existing RGB
UAV monitoring pipelines without additional depth sensors, pixel-level
segmentation, three-dimensional reconstruction, or retraining of the primary bush detector. The results demonstrate that
geometric reasoning applied to existing detector outputs can complement deep-learning-based object detection and improve plant-level interpretation in dense agricultural canopies.
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All authors Ik Jae Lee, Hieu D. Nguyen, Mahbubur Meenar, Carlos Morrison Martinez, Cameron Connelly
Computer Vision
Beyond Exact Match: Task-Aware GRPO for Cross-Domain PCBA Visual Question Answering
Jia Li, Li Dai, Peng Jia et al. (+4 more)
In automated Printed
Circuit Board Assembly (PCBA) inspection, standards-guided decisions require systems to jointly reason over fine-grained visual cues, component semantics, and manufacturing knowledge. Although large
vision-
language models (VLMs) provide a promising foundation, their deployment is hindered by the domain shift between standards-derived samples and real-world production-line imagery, together with heterogeneous output spaces spanning choice-based and numerical counting tasks. To address these challenges, we propose a
multimodal reasoning framework for cross-domain PCBA visual question answering. The framework converts standards-derived, real-world, and auxiliary PCB-domain data into a unified instruction format and constructs verified
reasoning traces aligned with visual evidence, question semantics, candidate options, and ground-truth answers. We further introduce Task-Aware Group Relative Policy Optimization (GRPO), which moves beyond exact-match supervision by integrating multi-component semantic rewards for choice-based questions, distance-aware rewards for counting questions, and an auxiliary format reward for valid outputs. During inference, answer-option semantic consistency correction, self-consistency voting, and multi-model arbitration are combined to improve prediction robustness. The proposed system achieves an Overall Score of 83.24 on the official PCBA Standard-to-Real Grand Challenge leaderboard, demonstrating the effectiveness of task-aware reward design and robust inference for cross-domain PCBA visual question answering.
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All authors Jia Li, Li Dai, Peng Jia, Zhenzhen Hu, Chee Seng Chan, Bingkun Bao, Richang Hong
Computer Vision
Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing
Chongbo Zhao, Jiangming Wang, Xilai Wang et al. (+5 more)
Text-guided
video editing modifies target content while preserving the appearance and temporal coherence of unedited regions. Training-based approaches provide strong control but demand substantial data and computation. Training-free methods fall into inversion-free and inversion-based paradigms. Inversion-free approaches avoid trajectory recovery, but their source-preserving guidance can limit editing strength and leave semantic changes incomplete. Inversion-based approaches recover a latent trajectory before regeneration, where approximation errors can accumulate and cause source-content drift and temporal inconsistency. We introduce Edit-VAR, the first training-free and inversion-free framework for text-guided
video editing with a pretrained visual autoregressive
video model. Edit-VAR directly encodes the source
video into multi-scale discrete
tokens and performs probability-guided conditional
token replacement for source preservation.
Attention-guided
token-wise and scale-aware modulation selectively relaxes source constraints over edit-relevant positions and generation stages. Scale-Decoupled Generation, implemented as late-scale constraint release, regenerates motion-consistent details and reduces texture fragmentation. Residual-guided
token pruning further exploits redundancy at the final two high-resolution scales to reduce inference cost. Extensive experiments and a blind user study demonstrate that Edit-VAR outperforms existing training-free
video editing methods overall in editing fidelity, source preservation, temporal coherence, and inference efficiency.
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All authors Chongbo Zhao, Jiangming Wang, Xilai Wang, Xinyu Wang, Jingyi Tang, Chunjie Hao, Pengjie Song, Yue Ma
Computer Vision
Geometry-Aware Diffusion Guidance via Curvature-Adaptive Tubular Correction
Enze Jiang, Jinwei He and Zheng Ma
Gradient-guided
diffusion samplers provide flexible priors for inverse problems and conditional generation, but strong guidance can move the sampling trajectory into regions where the learned score is poorly supported. Existing tangent-projection strategies limit first-order departure from an iso-density surface, yet discard potentially useful normal motion and overlook the second-order departure induced by tangent motion on a curved surface. We introduce
curvature-adaptive tubular correction (CAT), a training-free plugin that regulates both effects within a shared, noise-dependent
geometric budget. CAT decomposes the guidance gradient into normal and tangent components, charges normal displacement at first order and tangent displacement according to directional
curvature, and obtains their jointly optimal magnitudes from a one-dimensional dual equation. Armijo backtracking calibrates the resulting finite step against the actual guidance objective, while
matrix-free directional derivatives avoid constructing the full score Jacobian. We establish local guarantees for the tubular approximation, uniqueness of the correction, and sufficient objective decrease. Across seven inverse problems on FFHQ and ImageNet, CAT improves the evaluated pixel- and latent-space host samplers, with particularly consistent gains in perceptual metrics. It also improves black hole reconstruction on InverseBench and yields the lowest FID among the compared methods at every tested classifier-free guidance scale, while maintaining stable saturation and contrast. These results support
curvature-aware tubular control as a reusable mechanism for stabilizing
diffusion guidance.
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Computer Vision · Cross-listed from cs.RO
VLA-Scope: Shift-Aware Failure Prediction for Vision-Language-Action Models
Kaiwen Zhu, Dongfang Liu and Liangkai Liu
Vision-
language-action (VLA) models map visual observations and natural-
language instructions to robotic actions, but distribution shifts can compromise their reliability. Because these models may still succeed under out-of-distribution (OOD) conditions, detecting OOD inputs alone is insufficient to predict execution failure. In this paper, we introduce VLA-Scope, a two-stage framework that combines input-shift characterization with execution history to predict failure during OOD rollouts. The first stage uses pooled image and
language representations to detect OOD inputs and classify their shift categories. For inputs flagged as OOD, the second stage combines the predicted category, action-prefix features, and execution progress features. A logistic regression model shared across shift categories updates failure risk as execution proceeds. We evaluate the framework with OpenVLA on ten LIBERO-Spatial tasks using leave-one-group-out cross-validation. OOD detection achieves a ROC-AUC of 0.9454, and shift classification achieves 91% accuracy. Evaluated independently of the OOD gate on all 1,400 OOD rollouts, the failure predictor achieves a ROC-AUC of 0.8497 after 60 executed actions, compared with 0.7906 without execution progress features. It also achieves a higher ROC-AUC than the evaluated ActProbe and SAFE-MLP baselines. These results suggest that combining action features with temporally aggregated execution step representations improves failure prediction under input shifts.
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Computer Vision
SafeStyle: Calibrated Style Residual Injection for Controllable Style-Leakage Trade-off in Diffusion Stylization
Zhangping Yang, Min Li, Song Yan et al. (+4 more)
Reference-guided
diffusion stylization aims to transfer visual style from a reference image while preserving the semantics specified by a text
prompt. However, image conditioning often entangles transferable style cues with reference-specific content, leading to an inherent trade-off: stronger conditioning improves style fidelity but increases content leakage, whereas aggressive suppression reduces leakage at the cost of style expression. This challenge is further complicated by the distinct spatial organization of texture- and geometry-dominant styles. To address these issues, we propose SafeStyle, a training-free framework for calibrated style residual injection in frozen
diffusion models. SafeStyle first estimates style-supported and content-associated subspaces from compact calibration sets, preserving their informative overlap while suppressing useless content variations. It then transports the purified style evidence over adaptive spatial granularity and constrains its effective influence through an explicit residual-norm budget. Experiments across texture- and geometry-dominant styles show that SafeStyle achieves a DINO style similarity of 0.432 while maintaining competitive text
alignment. On a semantically disjoint leakage-stress
benchmark, it further achieves a DINO style similarity of 0.474 with only 0.8% semantic leakage, demonstrating an effective balance between style fidelity and reference-content suppression.
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All authors Zhangping Yang, Min Li, Song Yan, Rong Gao, Xinliang Bi, Guanye Xiong, Yujie He
Computer Vision
Multiclass Semantic Segmentation of Wildland Fire Images Using Context-Aware Centralized Copy-Paste Data Augmentation
Joon Tai Kim, Nishanth Kunchala, Vishv Patel et al. (+5 more)
Producing accurate annotations for deep learning based image
segmentation is both costly and labor intensive. This challenge is especially evident in wildland fire applications, where accurately labeled
datasets are scarce due to the difficulty of collecting and annotating dynamic fire scenes. To address this problem, our previous work introduced the Centralized Copy-Paste Data Augmentation (CCPDA) method for semantic
segmentation of wildland fire imagery, which generates artificial training samples by randomly pasting fire clusters from source images onto target images. However, random placement can produce contextually unrealistic scenes, such as fire burning on asphalt. In this paper, we present a context-aware strategy designed specifically to improve data quality and realism in small multiclass wildland fire
datasets, ensuring that augmented samples remain contextually meaningful. The proposed method restricts fire placement to semantically valid target regions and selects the location whose Ash-Vegetation composition most closely matches the source context. This approach preserves existing fire regions in the target image, prevents unrealistic placements, and maintains contextual accuracy by generating images that resemble real wildland fire scenes. We evaluate the Context-Aware CCPDA strategy through numerical analysis and comparisons with other augmentation methods by a weighted sum-based multi-objective optimization (MOO) approach. The results confirm that the context-aware data augmentation strategy leads to improved
segmentation performance and contextual realism, outperforming other augmentation procedures.
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All authors Joon Tai Kim, Nishanth Kunchala, Vishv Patel, Tianle Chen, Ziyu Dong, Daniel Ospina Acero, Roger Williams, Mrinal Kumar
Computer Vision · Cross-listed from cs.RO
FOCAL-VLA: Subtask-Guided Geometry Distillation and Implicit World Modeling for Vision-Language-Action Models
Zhiyuan Gao, Di Wen, Yanxiang Zhan et al. (+4 more)
Vision-
language-action (VLA) models built on pretrained
vision-
language models have demonstrated strong performance across diverse robotic manipulation tasks. However, VLA models that directly map current 2D observations to actions often lack sufficient spatial and temporal understanding, limiting their performance in precise and long-horizon manipulation. Recent methods enhance VLA models through
geometric supervision and future-state prediction across the entire scene. However, these methods can suffer from redundant scene information, distracting the model from learning the geometry and dynamics relevant to the current interaction. To address this issue, we propose FOCAL-VLA, a framework that combines subtask-guided geometry
distillation with implicit world modeling to learn representations of current spatial structure and future interaction dynamics. To focus
geometric learning on the current subtask, we transfer
geometric knowledge from VGGT to the VLA model by aligning geometry latents with features from subtask-relevant image regions. To capture the future 3D
evolution of the current interaction, we incorporate implicit world modeling using Track4World features from current and future demonstration frames. The two complementary representations jointly guide action generation without running VGGT or Track4World at inference time. Experiments show that FOCAL-VLA outperforms baselines on both simulation
benchmarks and real-world manipulation tasks. Project website: https://zhiyuan-gao.github.io/FOCAL-VLA/.
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All authors Zhiyuan Gao, Di Wen, Yanxiang Zhan, Mohammad Khoshnazar, Jeroen Schäfer, Kunyu Peng, Michael Beetz
Computer Vision
VGGT-CAD: Reconstructing Parametric CAD 3D Model with Geometric Grounding
Chunan Yu, Tianrun Chen, Fu Shen et al. (+3 more)
Parametric CAD reconstruction requires recovering both precise geometry and editable modeling operations from visual observations, making it challenging under limited and ambiguous views. Existing methods mainly rely on 2D appearance cues and lack strong multi-view
geometric priors. In this work, we present VGGT-CAD, a geometry-aware framework for parametric CAD reconstruction from single- and multi-view observations. We transfer pretrained 3D
geometric priors into CAD reconstruction by encoding camera parameters as condition
tokens and jointly modeling them with image
tokens. To handle varying numbers of viewpoints, we introduce a variable-view cross-view context aggregation module that adaptively fuses multi-view features. We further develop a training-free geometry-aware view selection strategy to select complementary and reliable frames during inference. The resulting representation is decoded into CAD command sequences using a non-autoregressive decoder. We also develop VideoCAD, a large-scale multi-view
video benchmark derived from existing CAD data through multi-view re-rendering. Extensive experiments demonstrate the effectiveness of VGGT-CAD for visual CAD reconstruction under different observation configurations.
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All authors Chunan Yu, Tianrun Chen, Fu Shen, Cheng Chen, Lanyun Zhu, Yang Yang
Computer Vision
Multi-viewpoint Geo-localization with Event Cameras
Adam D. Hines, Michael Milford and Tobias Fischer
Robot localization is an ongoing challenge that demands mapping and positioning systems that are tolerant to viewpoint change. Event cameras are attracting increasing interest and adoption in robotics; however, dealing with viewpoint variance is an under-investigated problem in existing event-based localizers. In addition, event-based
datasets that emphasize viewpoint variance for challenging localization situations are scarce. Here, we introduce an event-based visual place recognition (VPR) system that performs robustly under viewpoint changes. We converted five large-scale geo-tagged
datasets, conventionally used to train frame-based localization systems, into synthetic event streams using Image-to-Event (I2E) conversion, and used them to fine-tune a pre-trained event-based
vision transformer backbone with a multi-loss function, yielding a system we call MegaEvent that learns viewpoint-robust features for place recognition. We achieved an average Recall@1 of 82% across three existing event-based localization
datasets, leading the next best event-based method by 20 recall points, and frame-based VPR models applied directly to event frames by 8 to 26 recall points. We introduce a new, challenging
dataset - Springfield-Event-VPR - which features a 3.7km walking route recorded in three camera orientations for a total of 11.1km, which MegaEvent outperforms the strongest baseline by 9 recall points. The code for MegaEvent is available at https://github.com/AdamDHines/megaevent.
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Computer Vision
Hand-Aware Transition Modeling for Bimanual Procedural Anomaly Detection
Di Wen, Jimmy Weissert, Luc Maria Scherrer et al. (+7 more)
Procedural anomaly detection in bimanual assembly requires judging each hand action against the execution so far. A corrective action may look unusual in isolation, while a visually plausible action can violate the order of the procedure. We present HACT, a transition model over predicted per-hand events. A role-preserving history keeps the concurrent responsibilities of both hands, and a marked temporal point process assigns each observed transition a semantic and temporal surprisal. A supervised evidence head and a two-state filter convert these surprisals into per-hand anomaly posteriors. A recovery-aware protocol on predicted events and participant-disjoint folds reports the recovery false-positive rate at an operating point selected on validation participants. On two bimanual power-tool procedures HACT has the highest AUPRC and F1 among the compared methods and the fewest recovery alarms. Applied without retraining to a different assembly order of the same product, it retains the highest AUPRC and F1. The source code is available at https://github.com/Kratos-Wen/HACT.
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All authors Di Wen, Jimmy Weissert, Luc Maria Scherrer, Cedric Zöllner, Kailun Yang, Ruiping Liu, Yufan Chen, Jiale Wei, Junwei Zheng, Kunyu Peng
Computer Vision
OnomatoBridge: Onomatopoeia Translation and Rendering Pipeline in Manga
Takara Taniguchi, Wataru Shimoda, Kota Yamaguchi et al. (+1 more)
Manga is a comic drawn by black and white paints gaining popularity around the world. Onomatopoeia in Manga specifically appeals to the audience with its unique visual styles, which convey sound, motion, and
emotion. Visual onomatopoeia translation requires the clean replacement of Japanese onomatopoeia with onomatopoeia in the other
language while preserving their visual style. Existing approaches often produce residual artifacts or style inconsistency when removing the Japanese onomatopoeia and rendering stylized English onomatopoeia. To approach these problems, we present OnomatoBridge, a filtering pipeline for visual onomatopoeia translation. We evaluate OnomatoBridge from Japanese to English on the Manga109 onomatopoeia
dataset and compare it with baseline image editing models. Experimental results show that the filtered outputs by the proposed method outperform those of conventional methods. OnomatoBridge improves English text correctness by roughly 10 to 25 points and reduces residual Japanese text by about 20 to 50% in relative terms.
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All authors Takara Taniguchi, Wataru Shimoda, Kota Yamaguchi, Hideki Nakayama
Computer Vision · Cross-listed from cs.RO
Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points
Junwan Choi, Woongrae Jo, Dong-Uk Seo et al. (+2 more)
Accurate initialization is essential for reliable visual-inertial odometry (VIO), but it is often ill-conditioned under degenerate motions. Existing methods typically require restrictive excitation motions to ensure sufficient observability or rely on computationally expensive 3D structure reconstruction, limiting efficient and practical deployment. To address these limitations, we propose SLIM-init, a structureless
monocular VIO initializer that directly exploits
geometric constraints from tracked 2D line features without explicit 3D landmark reconstruction. Specifically, SLIM-init leverages line-derived vanishing points (VPs) as translation-invariant orientation cues to provide robust rotation-only constraints under degenerate scenarios such as low-parallax or translation-dominant motions. It further incorporates a line epipolar residual to constrain translation and a line-normal projection residual to improve the conditioning of linear
alignment, enhancing the accuracy and robustness of initial state estimation. Extensive experiments on a public
benchmark and challenging custom degenerate-motion sequences demonstrate improved accuracy and robustness over state-of-the-art initialization methods. The source code is available at: https://github.com/cjunwan/SLIM-init.
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All authors Junwan Choi, Woongrae Jo, Dong-Uk Seo, Jinwoo Jeon, Hyun Myung
Computer Vision
4DGS-Fixer: Generative Sparse-View 4D Gaussian Splatting with Iterative Refinement Guided by Video Diffusion Priors
Haitao Huang, Shenghao Zhao, Boyuan Tian et al. (+7 more)
This paper addresses the challenges of dynamic scene synthesis from sparse-view
videos. Existing methods employ
geometric priors, adaptive optimization, or density-control strategies to improve 4D Gaussian modeling under sparse observations. However, they cannot fundamentally resolve the ill-posed problem caused by insufficient observations and missing scene information. Moreover, sparse-view 4D Gaussian Splatting (4DGS) often suffers from poor
geometric initialization: with only a few input views, COLMAP typically reconstructs sparse and incomplete point clouds, leaving large scene regions without sufficient Gaussian support and making them difficult to recover through subsequent optimization. To address these limitations, we propose a novel iterative refinement framework based on a
video diffusion model to improve the completeness and consistency of dynamic 4D scenes. Specifically, we first estimate multi-view depth maps and fuse them into dense point clouds to provide more complete
geometric initialization for a dynamic 4DGS representation. We then employ a pretrained
video restoration model to refine sequences rendered along novel camera trajectories at different time steps. The restored sequences serve as pseudo-supervision to regularize and iteratively refine the 4DGS representation. Experiments on a widely used
benchmark dataset demonstrate that our method substantially outperforms existing baselines, achieving nearly a 2 dB PSNR improvement over the previous best-performing method.
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All authors Haitao Huang, Shenghao Zhao, Boyuan Tian, Shin-Fang Chng, Songlin Yang, Sheila Lim, Huangying Zhan, Yi Xu, Anyi Rao, Frank Guan
Computer Vision · Cross-listed from eess.IV
Adaptive Color Grading
Trevor D. Canham, Abhijith Punnappurath and Michael S. Brown
Independent control of tonescale regions (e.g., shadows, highlights) is essential for painters, photographers and cinematographers to bring 2D images to life. In image manipulation software this is most directly addressed by color grading modules, which use intensity thresholds to segment distinct illumination regions for local manipulation. In this work we develop an open source color grading tool and use it to annotate a large
dataset of
video frames with tonescale region thresholds. Using these thresholds we conduct modeling experiments with strategies based on both practitioners' conventional wisdom and machine learning. Results show that K-nearest neighbors is an effective prediction strategy, outperforming state-of-the-art end-to-end methods for image enhancement. This outcome demonstrates the benefit of focusing on a compact set of core parameters when modeling creative stylization processes. Our adaptive color grading interface and data are available at https://github.com/SamsungLabs/adaptive-color-grading.
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