Computer Vision
3D Multi-View Stylization with Pose-Free Correspondences Matching for Robust 3D Geometry Preservation
Shirsha Bose
Artistic style transfer is well studied for images and
videos, but extending it to multi-view 3D scenes remains difficult because stylization can disrupt correspondences needed by geometry-aware pipelines. Independent per-view stylization often causes texture drift, warped edges, and inconsistent shading, degrading SLAM, depth prediction, and multi-view reconstruction. This thesis addresses multi-view stylization that remains usable for downstream 3D tasks without assuming camera poses or an explicit 3D representation during training. We introduce a feed-forward stylization network trained with per-scene test-time optimization under a composite objective coupling appearance transfer with geometry preservation. Stylization is driven by an AdaIN-inspired loss from a frozen VGG-19 encoder, matching channel-wise moments to a style image. To stabilize structure across viewpoints, we propose a correspondence-based consistency loss using SuperPoint and SuperGlue, constraining descriptors from a stylized anchor view to remain consistent with matched descriptors from the original multi-view set. We also impose a depth-preservation loss using MiDaS/DPT and use global color
alignment to reduce depth-model domain shift. A staged weight schedule introduces geometry and depth constraints. We evaluate on Tanks and Temples and Mip-NeRF 360 using image and reconstruction metrics. Style adherence and structure retention are measured by Color Histogram Distance (CHD) and Structure Distance (DSD). For 3D consistency, we use
monocular DROID-SLAM trajectories and symmetric Chamfer distance on back-projected point clouds. Across ablations, correspondence and depth regularization reduce structural distortion and improve SLAM stability and reconstructed geometry; on scenes with MuVieCAST baselines, our method yields stronger trajectory and point-cloud consistency while maintaining competitive stylization.
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Computer Vision
SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging
Guifeng Deng, Pan Wang, Mengfan Niu et al. (+9 more)
Sleep staging is essential for sleep assessment and disorder diagnosis. In recent years, automatic sleep staging systems have achieved accuracy approaching that of human experts, but the black-box nature of their predictions hinders clinical adoption. Existing interpretability methods offer partial insight into model behavior, but their outputs still require expert reinterpretation and do not provide a direct basis for
auditing individual predictions. To improve trustworthiness, we propose the task of auditable sleep staging. To solve this task, we present SleepVLM, a
vision-
language model that casts sleep staging as visual
reasoning over rendered polysomnography (PSG) waveform images. For each epoch, SleepVLM outputs a stage together with the applicable American Academy of Sleep Medicine (AASM) rules and an auditable rationale. The model is trained using a two-stage framework: Waveform-Perceptual Pre-training followed by Rule-Grounded Supervised Fine-tuning over a mixture of fine-grained and coarse annotations. Experiments on four
datasets show that SleepVLM outperforms state-of-the-art methods on average. An automated AASM-feature audit shows broad coverage of stage-defining evidence in the rationales, and independent experts validate their
reasoning quality. To facilitate further research, we construct and release MASS-EX, an expert-annotated
dataset for rule-grounded sleep staging with AASM rule annotations and expert-written rationales.
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All authors Guifeng Deng, Pan Wang, Mengfan Niu, Jiquan Wang, Shuying Rao, Junyi Xie, Xi'ang Chen, Sha Zhao, Gang Pan, Wanjun Guo, Tao Li, Haiteng Jiang
Computer Vision
PAS3R: Pose-Adaptive Streaming 3D Reconstruction for Long Video Sequences
Lanbo Xu, Liang Guo, Caigui Jiang et al. (+1 more)
Online
monocular 3D reconstruction enables dense scene recovery from streaming
video but remains fundamentally limited by the stability-adaptation dilemma: the reconstruction model must rapidly incorporate novel viewpoints while preserving previously accumulated scene structure. Existing streaming approaches rely on uniform or
attention-based update mechanisms that often fail to account for abrupt viewpoint transitions, leading to trajectory drift and
geometric inconsistencies over long sequences. We introduce PAS3R, a pose-adaptive streaming reconstruction framework that dynamically modulates state updates according to camera motion and scene structure. Our key insight is that frames contributing significant
geometric novelty should exert stronger influence on the reconstruction state, while frames with minor viewpoint variation should prioritize preserving historical context. PAS3R operationalizes this principle through a motion-aware update mechanism that jointly leverages inter-frame pose variation and image frequency cues to estimate frame importance. To further stabilize long-horizon reconstruction, we introduce trajectory-consistent training objectives that incorporate relative pose constraints and acceleration regularization. A lightweight online stabilization module further suppresses high-frequency trajectory jitter and
geometric artifacts without increasing
memory consumption. Extensive experiments across multiple
benchmarks demonstrate that PAS3R significantly improves trajectory accuracy, depth estimation, and point cloud reconstruction quality in long
video sequences while maintaining competitive performance on shorter sequences.
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All authors Lanbo Xu, Liang Guo, Caigui Jiang, Cheng Wang
Computer Vision
Image-Based Structural Analysis Using Computer Vision and LLMs: PhotoBeamSolver
Altamirano-Muñiz Emilio Fernando
This paper presents the development of a documented program capable of solving idealized beam models, such as those commonly used in textbooks and academic exercises, from drawings made by a person. The system is based on computer
vision and statistical learning techniques for the detection and visual interpretation of structural elements. Likewise, the main challenges and limitations associated with the integration of computer
vision into structural analysis are analyzed, as well as the requirements necessary for its reliable application in the field of civil engineering. In this context, the implementation of the PhotoBeamSolver program is explored, and the current state of computer
vision in civil engineering is discussed, particularly in relation to structural analysis, infrastructure inspection, and engineering decision-support systems.
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Computer Vision
Jingchen Sun, Shaobo Han, Deep Patel et al. (+3 more)
Knowledge
distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from data and learning from the teacher is challenging, as some samples may be noisy while others are subject to teacher uncertainty. This motivates the need for adaptively balancing data and teacher supervision. We propose Beta-weighted Knowledge
Distillation (Beta-KD), an uncertainty-aware
distillation framework that adaptively modulates how much the student relies on teacher guidance. Specifically, we formulate teacher–student learning from a unified
Bayesian perspective and interpret teacher supervision as a Gibbs prior over student activations. This yields a closed-form, uncertainty-aware weighting mechanism and supports arbitrary
distillation objectives and their combinations. Extensive experiments on
multimodal VQA
benchmarks demonstrate that distilling student
Vision-
Language Models from a large teacher VLM consistently improves performance. The results show that Beta-KD outperforms existing knowledge
distillation methods. The code is available at https://github.com/Jingchensun/beta-kd.
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All authors Jingchen Sun, Shaobo Han, Deep Patel, Wataru Kohno, Can Jin, Changyou Chen
Computer Vision
Knowledge Priors for Identity-Disentangled Open-Set Privacy-Preserving Video FER
Feng Xu, Xun Li, Lars Petersson et al. (+3 more)
Facial expression recognition relies on facial data that inherently expose identity and thus raise significant
privacy concerns. Current
privacy-preserving methods typically fail in realistic open-set
video settings where identities are unknown, and identity labels are unavailable. We propose a two-stage framework for
video-based
privacy-preserving FER in challenging open-set settings that requires no identity labels at any stage. To decouple
privacy and utility, we first train an identity-suppression network using intra- and inter-
video knowledge priors derived from real-world
videos without identity labels. This network anonymizes identity while preserving expressive cues. A subsequent denoising module restores expression-related information and helps recover FER performance. Furthermore, we introduce a falsification-based validation method that uses recognition priors to rigorously evaluate
privacy robustness without requiring annotated identity labels. Experiments on three
video datasets demonstrate that our method effectively protects
privacy while maintaining FER accuracy comparable to identity-supervised baselines.
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All authors Feng Xu, Xun Li, Lars Petersson, Yulei Sui, David Ahmedt-Aristizabal, Dadong Wang
Computer Vision
Mitigating Objectness Bias and Region-to-Text Misalignment for Open-Vocabulary Panoptic Segmentation
Nikolay Kormushev, Josip Šarić and Matej Kristan
Open-vocabulary panoptic
segmentation remains hindered by two coupled issues: (i) mask selection bias, where objectness heads trained on closed vocabularies suppress masks of categories not observed in training, and (ii) limited regional understanding in
vision-
language models such as CLIP, which were optimized for global image classification rather than localized
segmentation. We introduce OVRCOAT, a simple, modular framework that tackles both. First, a CLIP-conditioned objectness adjustment (COAT) updates background/foreground probabilities, preserving high-quality masks for out-of-vocabulary objects. Second, an open-vocabulary mask-to-text refinement (OVR) strengthens CLIP's region-level
alignment to improve classification of both seen and unseen classes with markedly lower
memory cost than prior fine-tuning schemes. The two components combine to jointly improve objectness estimation and mask recognition, yielding consistent panoptic gains. Despite its simplicity, OVRCOAT sets a new state of the art on ADE20K (+5.5% PQ) and delivers clear gains on Mapillary Vistas and Cityscapes (+7.1% and +3% PQ, respectively). The code is available at: https://github.com/nickormushev/OVRCOAT
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Computer Vision
An InSAR Phase Unwrapping Framework for Large-scale and Complex Events
Yijia Song, Juliet Biggs, Alin Achim et al. (+3 more)
Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and abrupt displacement discontinuities, which severely disrupt phase continuity and often cause conventional unwrapping
algorithms to fail. Another limitation of existing learning-based unwrapping methods is their reliance on fixed and relatively small input sizes, while real InSAR interferograms are typically large-scale and spatially heterogeneous. This mismatch restricts the applicability of many neural network approaches to real-world data. In this work, we present a phase unwrapping framework based on a
diffusion model, developed to process large-scale interferograms and to address phase discontinuities caused by deformation. By leveraging a
diffusion model architecture, the proposed method can recover physically consistent unwrapped phase fields even in the presence of fault-related phase jumps. Experimental results on both synthetic and real
datasets demonstrate that the method effectively addresses discontinuities associated with near-surface deformation and scales well to large InSAR images, offering a practical alternative to manual unwrapping in challenging scenarios.
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All authors Yijia Song, Juliet Biggs, Alin Achim, Robert Popescu, Simon Orrego, Nantheera Anantrasirichai
Computer Vision
HamVision: Hamiltonian Dynamics as Inductive Bias for Medical Image Analysis
Mohamed A Mabrok
We present HamVision, a framework for medical image analysis that uses the damped harmonic oscillator, a fundamental building block of signal processing, as a structured inductive bias for both
segmentation and classification tasks. The oscillator's phase-space decomposition yields three functionally distinct representations: position~$q$ (feature content), momentum~$p$ (spatial gradients that encode boundary and texture information), and energy $H = \tfrac{1}{2}|z|^2$ (a parameter-free saliency map). These representations emerge from the dynamics, not from supervision, and can be exploited by different task-specific heads without any modification to the oscillator itself. For
segmentation, energy gates the skip connections while momentum injects boundary information at every decoder level (HamSeg). For classification, the three representations are globally pooled and concatenated into a phase-space feature vector (HamCls). We evaluate HamVision across ten medical imaging
benchmarks spanning five imaging modalities. On
segmentation, HamSeg achieves state-of-the-art Dice scores on ISIC\,2018 (89.38%), ISIC\,2017 (88.40%), TN3K (87.05%), and ACDC (92.40%), outperforming most baselines with only 8.57M parameters. On classification, HamCls achieves state-of-the-art accuracy on BloodMNIST (98.85%) and PathMNIST (96.65%), and competitive results on the remaining MedMNIST
datasets against MedMamba and MedViT. Diagnostic analysis confirms that the oscillator's momentum consistently encodes an interior$\,{>}\,$boundary$\,{>}\,$exterior gradient for
segmentation and that the energy map correlates with discriminative regions for classification, properties that emerge entirely from the
Hamiltonian dynamics. Code is available at https://github.com/Minds-R-Lab/hamvision.
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Computer Vision
Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation
Zengqun Zhao, Yanzuo Lu, Ziquan Liu et al. (+3 more)
Autoregressive
video diffusion has recently emerged as a promising paradigm for long-
video generation, enabling causal synthesis beyond the temporal limits of bidirectional models. Existing forcing-based training strategies reduce exposure bias by conditioning models on their own predictions during rollout, yet minute-scale generation remains challenging due to progressive temporal degradation and constrained motion
evolution. In this work, we study the role of temporal KV
memory during long-horizon autoregressive inference. Our analysis shows that simply retaining more historical frames does not consistently improve generation quality; instead, both the quantity and temporal placement of
memory strongly affect motion dynamics. These findings suggest that temporal
memory should be treated as structured context rather than a homogeneous chronological buffer. Motivated by this observation, we introduce Relax Forcing, a training-free
memory mechanism for autoregressive
video diffusion. Relax Forcing decomposes temporal context into three functional components: Sink frames that provide global stability, Tail frames that preserve short-term continuity, and dynamically selected History frames that supply mid-range motion structure. History frames are selected using a relaxation-based criterion that encourages
alignment with global anchors while suppressing redundancy with recent context. This role-aware sparse
memory design mitigates error accumulation during long-horizon rollout while preserving motion
evolution and reducing
attention overhead. Experiments on VBench-Long show that Relax Forcing improves long-
video generation, achieving stronger motion dynamics and higher overall scores than existing autoregressive baselines. These results indicate that structured temporal
memory is an effective and complementary direction for scalable long-
video generation.
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All authors Zengqun Zhao, Yanzuo Lu, Ziquan Liu, Jifei Song, Jiankang Deng, Ioannis Patras
Computer Vision
FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction
Yuqiu Liu, Jialin Song, Marissa Ramirez de Chanlatte et al. (+5 more)
Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train with photometric losses and reason about uncertainty in the image or representation space. This appearance-centric view overlooks body contacts and couplings, conflates function-critical regions (e.g., aerodynamic or hydrodynamic surfaces) with ornamentation, and reconstructs structures suboptimally, even when physical regularizers are added. All these can lead to unphysical and implausible interactions. To address this, we consider the question: How can 3D reconstruction become aware of real-world interactions and underlying object functionality, beyond visual cues? To answer this question, we propose FluidGaussian, a plug-and-play method that tightly couples geometry reconstruction with ubiquitous fluid-structure interactions to assess surface quality at high granularity. We define a simulation-based uncertainty metric induced by fluid simulations and integrate it with active learning to prioritize views that improve both visual and physical fidelity. In an empirical evaluation on NeRF Synthetic (Blender), Mip-NeRF 360, and DrivAerNet++, our FluidGaussian method yields up to +8.6% visual PSNR (Peak Signal-to-Noise Ratio) and -62.3% velocity divergence during fluid simulations. Our code is available at https://github.com/delta-lab-ai/FluidGaussian.
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All authors Yuqiu Liu, Jialin Song, Marissa Ramirez de Chanlatte, Rochishnu Chowdhury, Rushil Paresh Desai, Wuyang Chen, Daniel Martin, Michael W. Mahoney
Computer Vision
Respiratory Status Detection with Video Transformers
Thomas Savage and Evan Madill
Recognition of respiratory distress through visual inspection is a life saving clinical skill. Clinicians can detect early signs of respiratory deterioration, creating a valuable window for earlier intervention. In this study, we evaluate whether recent advances in
video transformers can enable Artificial Intelligence systems to recognize the signs of respiratory distress from
video. We collected
videos of healthy volunteers recovering after strenuous exercise and used the natural recovery of each participants respiratory status to create a labeled
dataset for respiratory distress. Splitting the
video into short clips, with earlier clips corresponding to more shortness of breath, we designed a temporal ordering challenge to assess whether an AI system can detect respiratory distress. We found a ViViT encoder augmented with Lie Relative Encodings (LieRE) and Motion Guided Masking, combined with an
embedding based comparison strategy, can achieve an F1 score of 0.81 on this task. Our findings suggest that modern
video transformers can recognize subtle changes in respiratory mechanics.
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Computer Vision
Efficient Coarse-to-Fine Diffusion Models with Time Step Sequence Redistribution
Yu-Shan Tai and An-Yeu Wu
Recently,
diffusion models (DMs) have made significant strides in high-quality image generation. However, the multi-step denoising process often results in considerable computational overhead, impeding deployment on resource-constrained edge devices. Existing methods mitigate this issue by compressing models and adjusting the time step sequence. However, they overlook input redundancy and require lengthy search times. In this paper, we propose Coarse-to-Fine
Diffusion Models with Time Step Sequence Redistribution. Recognizing indistinguishable early-stage generated images, we introduce Coarse-to-Fine Denoising (C2F) to reduce computation during coarse feature generation. Furthermore, we design Time Step Sequence Redistribution (TRD) for efficient sampling trajectory adjustment, requiring less than 10 minutes for search. Experimental results demonstrate that the proposed methods achieve near-lossless performance with an 80% to 90% reduction in computation on CIFAR10 and LSUN-Church.
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Computer Vision
EmoTaG: Emotion-Aware Talking Head Synthesis on Gaussian Splatting with Few-Shot Personalization
Haolan Xu, Keli Cheng, Lei Wang et al. (+2 more)
Audio-driven 3D talking head synthesis has advanced rapidly with Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). By leveraging rich pre-trained priors, few-shot methods enable instant personalization from just a few seconds of
video. However, under expressive facial motion, existing few-shot approaches often suffer from
geometric instability and audio-
emotion mismatch, highlighting the need for more effective
emotion-aware motion modeling. In this work, we present EmoTaG, a few-shot
emotion-aware 3D talking head synthesis framework built on the Pretrain-and-Adapt paradigm. Our key insight is to reformulate motion prediction in a structured FLAME parameter space rather than directly deforming 3D Gaussians, thereby introducing explicit
geometric priors that improve motion stability. Building upon this, we propose a Gated Residual Motion Network (GRMN), which captures emotional prosody from audio while supplementing head pose and upper-face cues absent from audio, enabling expressive and coherent motion generation. Extensive experiments demonstrate that EmoTaG achieves state-of-the-art performance in emotional expressiveness, lip synchronization, visual realism, and motion stability.
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All authors Haolan Xu, Keli Cheng, Lei Wang, Ning Bi, Xiaoming Liu
Computer Vision
KHMP: Frequency-Domain Kalman Refinement for High-Fidelity Human Motion Prediction
Wenhan Wu, Zhishuai Guo, Chen Chen et al. (+4 more)
Stochastic human motion prediction aims to generate diverse, plausible futures from observed sequences. Despite advances in generative modeling, existing methods often produce predictions corrupted by high-frequency jitter and temporal discontinuities. To address these challenges, we introduce KHMP, a novel framework featuring an adaptiveKalman filter applied in the DCT domain to generate high-fidelity human motion predictions. By treating high-frequency DCT coefficients as a frequency-indexed noisy signal, the Kalman filter recursively suppresses noise while preserving motion details. Notably, its noise parameters are dynamically adjusted based on estimated Signal-to-Noise Ratio (SNR), enabling aggressive denoising for jittery predictions and conservative filtering for clean motions. This refinement is complemented by training-time physical constraints (temporal smoothness and joint angle limits) that encode biomechanical principles into the generative model. Together, these innovations establish a new paradigm integrating adaptive signal processing with physics-informed learning. Experiments on the Human3.6M and HumanEva-I
datasets demonstrate that KHMP achieves state-of-the-art accuracy, effectively mitigating jitter artifacts to produce smooth and physically plausible motions.
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All authors Wenhan Wu, Zhishuai Guo, Chen Chen, Srijan Das, Hongfei Xue, Pu Wang, Aidong Lu
Computer Vision
Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos
Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi et al. (+3 more)
Facial expression recognition (FER) in
videos requires model personalization to capture considerable variation across subjects.
Vision-
language models (VLMs) offer strong transfer through image-text
alignment, but their performance can degrade under inter-subject distribution shifts. Test-time adaptation (TTA) can mitigate this challenge, yet most state-of-the-art methods rely on unsupervised parameter optimization, introducing computational overhead that limits real-world deployment. This paper introduces TTA through Cache Personalization (TTA-CaP), a gradient-free, cache-based method for cost-effective personalization of VLMs in
video FER. Unlike prior cache-based TTA methods that rely solely on dynamic
memories of test samples and may drift because of noisy pseudo-labels, TTA-CaP employs three complementary caches: a personalized static cache constructed through feature-statistics matching, a positive target cache containing reliable subject-specific samples, and a negative target cache containing low-confidence cases as negative evidence. A tri-gate mechanism prevents cache corruption by controlling updates according to temporal stability, confidence, and consistency with the personalized static cache. The caches jointly provide subject-matched positive and negative evidence for robust personalization. TTA-CaP further refines predictions through
embedding fusion, supporting temporally stable
video-level predictions. Experiments on BioVid, StressID, and BAH show that TTA-CaP outperforms state-of-the-art TTA methods under subject-specific and environmental shifts while maintaining low computational and
memory overhead. Our code is publicly available at https://github.com/MasoumehSharafi/TTA-CaP.
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All authors Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi, Alessandro Lameiras Koerich, Marco Pedersoli, Eric Granger
Computer Vision
Privacy-Preserving Federated Action Recognition via Differentially Private Selective Tuning and Efficient Communication
Idris Zakariyya, Pai Chet Ng, Kaushik Bhargav Sivangi et al. (+3 more)
Federated video action recognition enables collaborative model training without sharing raw
video data, yet remains vulnerable to two key challenges: model exposure and communication overhead. Gradients exchanged between clients and the server can leak private motion patterns, while full-model synchronization of high-dimensional
video networks causes significant bandwidth and communication costs. To address these issues, we propose
Federated Differential
Privacy with Selective Tuning and Efficient Communication for Action Recognition, namely FedDP-STECAR. Our FedDP-STECAR framework selectively fine-tunes and perturbs only a small subset of task-relevant layers under Differential
Privacy (DP), reducing the surface of information leakage while preserving temporal coherence in
video features. By transmitting only the tuned layers during aggregation, communication traffic is reduced by over 99% compared to full-model updates. Experiments on the UCF-101
dataset using the MViT-B-16x4
transformer show that FedDP-STECAR achieves up to 70.2% higher accuracy under strict
privacy ($\epsilon=0.65$) in centralized settings and 48% faster training with 73.1% accuracy in
federated setups, enabling scalable and
privacy-preserving
video action recognition. Code available at https://github.com/izakariyya/mvit-
federated-videodp
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All authors Idris Zakariyya, Pai Chet Ng, Kaushik Bhargav Sivangi, S. Mohammad Sheikholeslami, Konstantinos N. Plataniotis, Fani Deligianni
Computer Vision
F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
Injae Kim, Chaehyeon Kim, Minseong Bae et al. (+2 more)
Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uniformly allocate Gaussians, leading to redundant Gaussians across views. Moreover, they lack an effective mechanism to control the total number of Gaussians while maintaining reconstruction fidelity. To address these limitations, we present F4Splat, which performs Feed-Forward predictive densification for Feed-Forward 3D Gaussian Splatting, introducing a densification-score-guided allocation strategy that adaptively distributes Gaussians according to spatial complexity and multi-view overlap. Our model predicts per-region densification scores to estimate the required Gaussian density and allows explicit control over the final Gaussian budget without retraining. This spatially adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, producing compact yet high-quality 3D representations. Extensive experiments demonstrate that our model achieves superior novel-view synthesis performance compared to prior uncalibrated feed-forward methods, while using significantly fewer Gaussians.
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All authors Injae Kim, Chaehyeon Kim, Minseong Bae, Minseok Joo, Hyunwoo J. Kim
Computer Vision
Identity-Consistent Video Generation under Large Facial-Angle Variations
Bin Hu, Zipeng Qi, Guoxi Huang et al. (+6 more)
Single-view reference-to-
video methods often struggle to preserve identity consistency under large facial-angle variations. This limitation naturally motivates the incorporation of multi-view facial references. However, simply introducing additional reference images exacerbates the copy-paste problem, particularly the \textbf{view-dependent copy-paste} artifact, which reduces facial motion naturalness. Although cross-paired data can alleviate this issue, collecting such data is costly. To balance the consistency and naturalness, we propose $\mathrm{Mv}^2\mathrm{ID}$, a multi-view conditioned framework under in-paired supervision. We introduce a region-masking training strategy to prevent shortcut learning and extract essential identity features by encouraging the model to aggregate complementary identity cues across views. In addition, we design a reference decoupled-RoPE mechanism that assigns distinct positional encoding to
video and conditioning
tokens for better modeling of their heterogeneous properties. Furthermore, we construct a large-scale
dataset with diverse facial-angle variations and propose dedicated evaluation metrics for identity consistency and motion naturalness. Extensive experiments demonstrate that our method significantly improves identity consistency while maintaining motion naturalness, outperforming existing approaches trained with cross-paired data.
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All authors Bin Hu, Zipeng Qi, Guoxi Huang, Zunnan Xu, Ruicheng Zhang, Chongjie Ye, Jun Zhou, Xiu Li, Jingdong Wang
Computer Vision
Text-Image Conditioned 3D Generation
Jiazhong Cen, Jiemin Fang, Sikuang Li et al. (+8 more)
High-quality 3D assets are essential for VR/AR, industrial design, and entertainment, motivating growing interest in generative models that create 3D content from user
prompts. Most existing 3D generators, however, rely on a single conditioning modality: image-conditioned models achieve high visual fidelity by exploiting pixel-aligned cues but suffer from viewpoint bias when the input view is limited or ambiguous, while text-conditioned models provide broad semantic guidance yet lack low-level visual detail. This limits how users can express intent and raises a natural question: can these two modalities be combined for more flexible and faithful 3D generation? Our diagnostic study shows that even simple late fusion of text- and image-conditioned predictions outperforms single-modality models, revealing strong cross-modal complementarity. We therefore formalize Text-Image Conditioned 3D Generation, which requires joint
reasoning over a visual exemplar and a textual specification. To address this task, we introduce TIGON, a minimalist dual-branch baseline with separate image- and text-conditioned backbones and lightweight cross-modal fusion. Extensive experiments show that text-image conditioning consistently improves over single-modality methods, highlighting complementary
vision-
language guidance as a promising direction for future 3D generation research. Project page: https://jumpat.github.io/tigon-page
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All authors Jiazhong Cen, Jiemin Fang, Sikuang Li, Guanjun Wu, Chen Yang, Taoran Yi, Zanwei Zhou, Zhikuan Bao, Lingxi Xie, Wei Shen, Qi Tian
Computer Vision
Zhengxian Wu, Kai Shi, Chuanrui Zhang et al. (+10 more)
Recent progress in
multimodal large
language models has led to strong performance on
reasoning tasks, but these improvements largely rely on high-quality annotated data or teacher-model
distillation, both of which are costly and difficult to scale. To address this, we propose an unsupervised self-
evolution training framework for
multimodal reasoning that achieves stable performance improvements without using human-annotated answers or external reward models. For each input, we sample multiple
reasoning trajectories and jointly model their within group structure. We use the Actor's self-consistency signal as a training prior, and introduce a bounded Judge based modulation to continuously reweight trajectories of different quality. We further model the modulated scores as a group level distribution and convert absolute scores into relative advantages within each group, enabling more robust policy updates. Trained with Group Relative Policy Optimization (GRPO) on unlabeled data, our method consistently improves
reasoning performance and generalization on five mathematical
reasoning benchmarks, offering a scalable path toward self-evolving
multimodal models. The code are available at https://github.com/OPPO-Mente-Lab/LLM-Self-Judge.
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All authors Zhengxian Wu, Kai Shi, Chuanrui Zhang, Zirui Liao, Jun Yang, Ni Yang, Qiuying Peng, Luyuan Zhang, Hangrui Xu, Tianhuang Su, Zhenyu Yang, Haonan Lu, Haoqian Wang
Computer Vision
Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Prompting
Yuntian Bo, Yazhou Zhu, Piotr Koniusz et al. (+1 more)
Conventional few-shot medical image
segmentation (FSMIS) approaches face performance bottlenecks that hinder broader clinical applicability. Although the Segment Anything Model (SAM) exhibits strong category-agnostic
segmentation capabilities, its direct application to medical images often leads to over-
segmentation due to ambiguous anatomical boundaries. In this paper, we reformulate SAM-based FSMIS as a
prompt localization task and propose FoB (Focus on Background), a background-centric
prompt generator that provides accurate background
prompts to constrain SAM's over-
segmentation. Specifically, FoB bridges the gap between
segmentation and
prompt localization by category-agnostic generation of support background
prompts and localizing them directly in the query image. To address the challenge of
prompt localization for novel categories, FoB models rich contextual information to capture foreground-background spatial dependencies. Moreover, inspired by the inherent structural patterns of background
prompts in medical images, FoB models this structure as a constraint to progressively refine background
prompt predictions. Experiments on three diverse medical image
datasets demonstrate that FoB outperforms other baselines by large margins, achieving state-of-the-art performance on FSMIS, and exhibiting strong cross-domain generalization. Our code is available at https://github.com/primebo1/FoB_SAM.
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All authors Yuntian Bo, Yazhou Zhu, Piotr Koniusz, Haofeng Zhang