GenAI4ELab/papercli-papers huggingface dataset:
U^2Flow: Uncertainty-Aware Unsupervised Optical Flow Estimation
Authors: Xunpei Sun; Wenwei Lin; Yi Chang; Gang Chen
Venue: CVPR 2026 (main)

Abstract: Unsupervised optical flow methods typically lack reliable uncertainty estimation, limiting their robustness and interpretability. We propose U^ 2 Flow, the first recurrent unsupervised framework that jointly estimates optical flow and per-pixel uncertainty. The core innovation is a decoupled learning strategy that derives uncertainty supervision from augmentation consistency via a Laplace-based maximum likelihood objective, enabling stable training without ground truth. The predicted uncertainty is further integrated into the network to guide adaptive flow refinement and dynamically modulate the regional smoothness loss. Furthermore, we introduce an uncertainty-guided bidirectional flow fusion mechanism that enhances robustness in challenging regions. Extensive experiments on KITTI and Sintel demonstrate that U^ 2 Flow achieves state-of-the-art performance among unsupervised methods while producing highly reliable uncertainty maps, validating the effectiveness of our joint estimation paradigm. The code is available at https://github.com/sunzunyi/U2FLOW.
Back to Basics: Let Denoising Generative Models Denoise
Authors: Tianhong Li; Kaiming He
Venue: CVPR 2026 (main)

Abstract: Today's denoising diffusion models do not "denoise" in the classical sense, i.e., they do not directly predict clean images. Rather, the neural networks predict noise or a noised quantity. In this paper, we suggest that predicting clean data and predicting noised quantities are fundamentally different. According to the manifold assumption, natural data should lie on a low-dimensional manifold, whereas noised quantities do not. With this assumption, we advocate for models that directly predict clean data, which allows apparently under-capacity networks to operate effectively in very high-dimensional spaces. We show that simple, large-patch Transformers on pixels can be strong generative models: using no tokenizer, no pre-training, and no extra loss. Our approach is conceptually nothing more than "Just image Transformers", or JiT, as we call it. We report competitive results using JiT with large patch sizes of 16 and 32 on ImageNet at resolutions of 256 and 512, where predicting high-dimensional noised quantities can fail catastrophically. With our networks mapping back to the basics of the manifold, our research goes back to basics and pursues a self-contained paradigm for Transformer-based diffusion on raw natural data.
PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations
Authors: Mingqi Yuan; Tao Yu; Haolin Song; Bo Li; Xin Jin; Hua Chen; Wenjun Zeng
Venue: CVPR 2026 (main)

Abstract: Achieving efficient and robust whole-body control (WBC) is essential for enabling humanoid robots to perform complex tasks in dynamic environments. Despite the success of reinforcement learning (RL) in this domain, its sample inefficiency remains a significant challenge due to the intricate dynamics and partial observability of humanoid robots. To address this limitation, we propose **PvP**, a **P**roprioceptive-**P**rivileged contrastive learning framework that leverages the intrinsic complementarity between proprioceptive and privileged states. PvP learns compact and task-relevant latent representations without requiring hand-crafted data augmentations, enabling faster and more stable policy learning. To support systematic evaluation, we develop **SRL4Humanoid**, the first unified and modular framework that provides high-quality implementations of representative state representation learning (SRL) methods for humanoid robot learning. Extensive experiments on the LimX Oli robot across velocity tracking and motion imitation tasks demonstrate that PvP significantly improves sample efficiency and final performance compared to baseline SRL methods. Our study further provides practical insights into integrating SRL with RL for humanoid WBC, offering valuable guidance for data-efficient humanoid robot learning.
Scaling Dense Event-Stream Pretraining from Visual Foundation Models
Authors: Zhiwen Chen; Junhui Hou; Zhiyu Zhu; Jinjian Wu; Guangming Shi
Venue: CVPR 2026 (main)

Abstract: Learning versatile, fine-grained representations from irregular event streams is pivotal yet nontrivial, primarily due to the heavy annotation that hinders scalability in dataset size, semantic richness, and application scope. To mitigate this dilemma, we launch a novel self-supervised pretraining method that distills visual foundation models (VFMs) to push the boundaries of event representation at scale. Specifically, we curate an extensive synchronized image-event collection to amplify cross-modal alignment. Nevertheless, due to inherent mismatches in sparsity and granularity between image-event domains, existing distillation paradigms are prone to semantic collapse in event representations, particularly at high resolutions. To bridge this gap, we propose to extend the alignment objective to semantic structures provided off-the-shelf by VFMs, indicating a broader receptive field and stronger supervision. The key ingredient of our method is a structure-aware distillation loss that grounds higher-quality image-event correspondences for alignment, optimizing dense event representations. Extensive experiments demonstrate that our approach takes a great leap in downstream benchmarks, significantly surpassing traditional methods and existing pretraining techniques. This breakthrough manifests in enhanced generalization, superior data efficiency and elevated transferability. The source code will be available.
SemLT3D: Semantic-Guided Expert Distillation for Camera-only Long-Tailed 3D Object Detection
Authors: Hao Vo; Khoa Vo; Thinh Phan; Ngo Xuan Cuong; Gianfranco Doretto; Hien Nguyen; Anh Nguyen; Ngan Le
Venue: CVPR 2026 (main)

Abstract: Camera-only 3D object detection has emerged as a cost-effective and scalable alternative to LiDAR for autonomous driving, yet existing methods primarily prioritize overall performance while overlooking the severe long-tail imbalance inherent in real-world datasets. In practice, many rare but safety-critical categories such as children, strollers, or emergency vehicles are heavily underrepresented, leading to biased learning and degraded performance. This challenge is further exacerbated by pronounced inter-class ambiguity (e.g., visually similar subclasses) and substantial intra-class diversity (e.g., objects varying widely in appearance, scale, pose, or context), which together hinder reliable long-tail recognition. In this work, we introduce SemLT3D, a Semantic-Guided Expert Distillation framework designed to enrich the representation space for underrepresented classes through semantic priors. SemLT3D consists of: (1) a language-guided mixture-of-experts module that routes 3D queries to specialized experts according to their semantic affinity, enabling the model to better disentangle confusing classes and specialize on tail distributions; and (2) a semantic projection distillation pipeline that aligns 3D queries with CLIP-informed 2D semantics, producing more coherent and discriminative features across diverse visual manifestations. Although motivated by long-tail imbalance, the semantically structured learning in SemLT3D also improves robustness under broader appearance variations and challenging corner cases, offering a principled step toward more reliable camera-only 3D perception.
When Do Models Actually Decide? Mapping the Layer-Wise Decision Timeline in Pretrained Neural Networks
Authors: Minhyeok Lee
Venue: CVPR 2026 (main)

Abstract: Neural networks are often treated as monolithic black boxes that process all inputs uniformly through all layers. However, researchers intuitively wonder: do simple images require all 50 layers of ResNet-50, or is the prediction effectively decided much earlier? We investigate when pretrained models make up their minds during a forward pass by training linear probes at each layer of ResNet variants on ImageNet, without modifying the base model. Our findings reveal substantial computational heterogeneity across architectures. We discover pronounced bimodal patterns with distinct populations of early and late deciders: in deeper ResNets, 33--39% of samples stabilize within the first third of the network, while 39--42% are assigned to the final 30% of depth; across all three ResNets, the late fraction ranges from 39% to 54%. The decision layer is highly sensitive to stability criteria, with mean depths increasing from 2.6--4.1 (k=1) to 9.0--10.0 (k=4). Linear probe accuracy exhibits sharp jumps in final residual stages, reaching 73--75% for ResNet-50/101 and 65% for ResNet-18, indicating that semantic consolidation occurs late. These findings expose computational heterogeneity in standard inference and clarify both the promise and the limits of stability-based early exiting.
CoWTracker: Tracking by Warping instead of Correlation
Authors: Zihang Lai; Eldar Insafutdinov; Edgar Sucar; Andrea Vedaldi
Venue: CVPR 2026 (main)

Abstract: State-of-the-art point trackers typically rely on cost volumes to match features across frames, but this approach incurs quadratic complexity in spatial resolution, limiting scalability and efficiency. In this paper, we propose CoWTracker, a novel dense point tracker that eschews cost volumes in favor of warping. Inspired by recent advances in optical flow, our approach iteratively refines track estimates by warping features from the target frame to the query frame based on the current track estimate. Combined with a transformer that performs joint spatiotemporal reasoning across all tracks, our design establishes long-range correspondences without computing feature correlations. Our model is simple and achieves state-of-the-art performance on standard dense point tracking benchmarks, including TAP-Vid-DAVIS, TAP-Vid-Kinetics, and Robo-TAP. Remarkably, the model also excels at optical flow, sometimes outperforming specialized methods on the Sintel, KITTI, and Spring benchmarks. These results suggest that warping-based architectures can unify dense point tracking and optical flow estimation.
Understanding Temporal Logic Consistency in Video-Language Models through Cross-Modal Attention Discriminability
Authors: Chengzhi Li; Heyan Huang; Ping Jian; Zhen Yang; Yaning Tian; Zhongbin Guo
Venue: CVPR 2026 (main)

Abstract: Large language models (LLMs) often generate self-contradictory outputs, which severely impacts their reliability and hinders their adoption in practical applications. In video-language models (Video-LLMs), this phenomenon recently draws the attention of researchers. Specifically, these models fail to provide logically consistent responses to rephrased questions based on their grounding outputs. However, the underlying causes of this phenomenon remain underexplored. In this work, we adopt an interpretability-driven approach to analyze, statistically summarize, and intervention the potential factors of the phenomenon. We find that one of the primary reasons for the inconsistency in responses lies in the inability of cross-modal attention heads to effectively distinguish video tokens across different timestamps. To address this, we propose an attention enhancement method called **T**emporally **C**onditioned **A**ttention **S**harpening (**TCAS**), which constructs an enhancement objective based on attention distinctions to enhance the model's temporal resolution capability, thereby improving its temporal understanding logic consistency. Experimental results demonstrate that our method significantly enhances the temporal logic consistency of Video-LLMs. Further analyses reveal that our method indeed improves the temporal discriminability of attention heads, validating our conclusions. Additionally, our method even achieves performance improvements in general video temporal grounding tasks, suggesting that temporal logic consistency is an important factor in temporal understanding.
CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation
Authors: Xia Su; Ruiqi Chen; Benlin Liu; Jingwei Ma; Zonglin Di; Ranjay Krishna; Jon Froehlich
Venue: CVPR 2026 (main)

Abstract: Vision-Language Models (VLMs) have shown remarkable progress in Vision-Language Navigation (VLN), offering new possibilities for navigation decision-making that could benefit both robotic platforms and human users. However, real-world navigation is inherently conditioned by the agent's mobility constraints. For example, a sweeping robot cannot traverse stairs, while a quadruped can. We introduce Capability-Conditioned Navigation (CapNav), a benchmark designed to evaluate how well VLMs can navigate complex indoor spaces given an agent's specific physical and operational capabilities. CapNav defines five representative human and robot agents, each described with physical dimensions, mobility capabilities, and environmental interaction abilities. CapNav provides 45 real-world indoor scenes, 473 navigation tasks, and 2365 QA pairs to test if VLMs can traverse indoor environments based on agent capabilities. We evaluate 13 modern VLMs and find that current VLM's navigation performance drops sharply as mobility constraints tighten, and that even state-of-the-art models struggle with obstacle types that require reasoning on spatial dimensions. We conclude by discussing the implications for capability-aware navigation and the opportunities for advancing embodied spatial reasoning in future VLMs. The benchmark is available at https://makeabilitylab.github.io/CapNav/
Where, What, Why: Toward Explainable 3D-GS Watermarking
Authors: Mingshu Cai; Jiajun Li; Osamu Yoshie; Yuya Ieiri; Yixuan Li
Venue: CVPR 2026 (main)

Abstract: As 3D Gaussian Splatting becomes the de facto representation for interactive 3D assets, robust yet imperceptible watermarking is critical. We present a representation-native framework that separates where to write from how to preserve quality. A Trio-Experts module operates directly on Gaussian primitives to derive priors for carrier selection, while a Safety and Budget Aware Gate (SBAG) allocates Gaussians to watermark carriers--optimized for bit resilience under perturbation and bitrate budgets--and to visual compensators that are insulated from watermark loss. To maintain fidelity, we introduce a channel-wise group mask that controls gradient propagation for carriers and compensators, thereby limiting Gaussian parameter updates, repairing local artifacts, and preserving high-frequency details without increasing runtime. Our design yields view-consistent watermark persistence and strong robustness against common image distortions such as compression and noise, while achieving a favorable robustness-quality trade-off compared with prior methods. In addition, the decoupled finetuning provides per-Gaussian attributions that reveal where the message is carried and why those carriers are selected, enabling auditable explainability. Compared with state-of-the-art methods, our approach achieves a PSNR improvement of +0.83 dB and a bit-accuracy gain of +1.24%.
WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation
Authors: Wei Chow; Jiachun Pan; Yongyuan Liang; Mingze Zhou; Xue Song; Liyu Jia; Saining Zhang; Siliang Tang; Juncheng Li; Fengda Zhang; Weijia Wu; Hanwang Zhang; Tat-Seng Chua
Venue: CVPR 2026 (main)

Abstract: Recent unified multimodal models (UMMs) have achieved remarkable progress in visual comprehension and generation. However, existing datasets and benchmarks focus predominantly on single-turn interactions, overlooking the multi-turn, context-dependent nature of real-world image creation and editing. To bridge this gap, we introduce W  E  A  V  E, the first comprehensive suite for in-context interleaved cross-modality comprehension and generation, comprising two complementary components. W  E  A  V  E-100k is a large-scale dataset of 100,000 interleaved samples spanning over 370,000 dialogue turns and 500,000 images, encompassing comprehension, editing, and generation tasks that demand reasoning over prior context. W  E  A  V  E-Bench is a human-annotated benchmark of 100 items with 480 images, equipped with a hybrid VLM judge evaluation framework that jointly leverages reference images and original-image-instruction pairs to assess multi-turn generation, visual memory, and world-knowledge reasoning across diverse domains. Experiments show that training on W  E  A  V  E-100k substantially improves vision comprehension, image editing, and comprehension-generation collaboration, while further enabling the emergence of visual-memory capabilities in UMMs. Extensive evaluations on W  E  A  V  E-Bench reveal persistent limitations of current approaches in multi-turn, context-aware image generation and editing. We hope W  E  A  V  E provides both a perspective and a foundation for advancing in-context interleaved comprehension and generation in the multimodal community.
Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals
Authors: Kunzhe Song; Geo Jie Zhou; Xiaoming Liu; Huacheng Zeng
Venue: CVPR 2026 (main)

Abstract: Robust 3D environmental perception is critical for applications such as autonomous driving and robot navigation. However, optical sensors such as cameras and LiDAR often fail under adverse conditions, including smoke, fog, and non-ideal lighting. Although specialized radar systems can operate in these environments, their reliance on bespoke hardware and licensed spectrum limits scalability and cost-effectiveness. This paper introduces Rascene, an integrated sensing and communication (ISAC) framework that leverages ubiquitous mmWave OFDM communication signals for 3D scene imaging. To overcome the sparse and multipath-ambiguous nature of individual radio frames, Rascene performs multi-frame, spatially adaptive fusion with confidence-weighted forward projection, enabling the recovery of geometric consensus across arbitrary poses. Experimental results demonstrate that our method reconstructs 3D scenes with high precision, offering a new pathway toward low-cost, scalable, and robust 3D perception.
Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation
Authors: Chuancheng Shi; Shangze Li; Shiming Guo; Simiao Xie; Wenhua Wu; Jingtong Dou; Chao Wu; Canran Xiao; Cong Wang; Zifeng Cheng; Fei Shen; Tat-Seng Chua
Venue: CVPR 2026 (main)

Abstract: Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilised. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural consistency. We conduct a comprehensive analysis showing that current T2I models often produce culturally neutral or English-biased results under multilingual prompts.Analyses of two representative models indicate that the issue stems not from missing cultural knowledge but from insufficient activation of culture-related representations. We propose a probing method that localizes culture-sensitive signals to a small set of neurons in a few fixed layers. Guided by this finding, we introduce two complementary alignment strategies: (1) inference-time cultural activation that amplifies the identified neurons without backbone fine-tuned; and (2) layer-targeted cultural enhancement that updates only culturally relevant layers. Experiments on our CultureBench demonstrate consistent improvements over strong baselines in cultural consistency while preserving fidelity and diversity.
Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection
Authors: Kaiqiang Li; Gang Li; Mingle Zhou; Min Li; Delong Han; Jin Wan
Venue: CVPR 2026 (main)

Abstract: Zero-shot (ZS) 3D anomaly detection is crucial for reliable industrial inspection, as it enables detecting and localizing defects without requiring any target-category training data. Existing approaches render 3D point clouds into 2D images and leverage pre-trained Vision-Language Models (VLMs) for anomaly detection. However, such strategies inevitably discard geometric details and exhibit limited sensitivity to local anomalies.  In this paper, we revisit intrinsic 3D representations and explore the potential of pre-trained Point-Language Models (PLMs) for ZS 3D anomaly detection. We propose BTP (Back To Point), a novel framework that effectively aligns 3D point cloud and textual embeddings. Specifically, BTP aligns multi-granularity patch features with textual representations for localized anomaly detection, while incorporating geometric descriptors to enhance sensitivity to structural anomalies. Furthermore, we introduce a joint representation learning strategy that leverages auxiliary point cloud data to improve robustness and enrich anomaly semantics. Extensive experiments on Real3D-AD and Anomaly-ShapeNet demonstrate that BTP achieves superior performance in ZS 3D anomaly detection.
Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation
Authors: Daikun Liu; Teng Wang; Changyin Sun
Venue: CVPR 2026 (main)

Abstract: Event cameras hold excellent dynamic properties, showing great potential for monocular depth estimation (MDE). However, existing methods mainly improve performance by optimizing contextual features, but still struggle with the ill-posed and nonlinear nature of direct full-depth regression. In this paper, we propose HypoDepth, the first event-image monocular depth iterative refinement framework. By introducing a discrete Depth Hypothesis Volume (DHV), we transform the depth regression problem into a constrained depth search task. Specifically, we construct a 3D cost volume between the DHV features and contextual features and perform a multi-scale correlation search to guide stable residual optimization. This lightweight cost volume enables efficient global-to-local refinement across multi-resolution. Our method outperforms existing approaches on DSEC and MVSEC with state-of-the-art results and strong zero-shot generalization. Meanwhile, our tiny model achieves an excellent balance between accuracy and efficiency, enabling real-time performance on resource-limited devices.
Transition Matching Distillation for Fast Video Generation
Authors: Weili Nie; Julius Berner; Nanye Ma; Chao Liu; Saining Xie; Arash Vahdat
Venue: CVPR 2026 (main)

Abstract: Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to their inefficient multi-step sampling process. In this work, we present Transition Matching Distillation (TMD), a novel framework for distilling video diffusion models into efficient few-step generators. The central idea of TMD is to match the multi-step denoising trajectory of a diffusion model with a few-step probability transition process, where each transition is modeled as a lightweight conditional flow. To enable efficient distillation, we decompose the original diffusion backbone into two components: (1) a main backbone, comprising the majority of early layers, that extracts semantic representations at each outer transition step; and (2) a flow head, consisting of the last few layers, that leverages these representations to perform multiple inner flow updates. Given a pretrained video flow model, we first introduce a flow head to the model, and adapt it into a conditional flow map. We then apply distribution matching distillation to the student model with flow head rollout in each transition step. Extensive experiments on distilling Wan2.1 1.3B and 14B text-to-video models demonstrate that TMD provides a flexible and strong trade-off between generation speed and visual quality. In particular, TMD outperforms existing distilled models under comparable inference costs in terms of visual fidelity and prompt adherence.