GenAI4ELab/papercli-papers huggingface dataset:
UniCompress: Token Compression for Unified Vision-Language Understanding and Generation
Authors: Ziyao Wang; Chen Chen; Jingtao Li; Weiming Zhuang; Jiabo Huang; Ang Li; Lingjuan Lyu
Venue: CVPR 2026 (main)

Abstract: Unified models aim to support both understanding and generation by encoding images into discrete tokens and processing them alongside text within a single autoregressive framework. This unified design offers architectural simplicity and cross-modal synergy, which facilitates shared parameterization, consistent training objectives, and seamless transfer between modalities. However, the large number of visual tokens required by such models introduces substantial computation and memory overhead, and this inefficiency directly hinders deployment in resource constrained scenarios such as embodied AI systems. In this work, we propose a unified token compression algorithm UniCompress that significantly reduces visual token count while preserving performance on both image understanding and generation tasks. Our method introduces a plug-in compression and decompression mechanism guided with learnable global meta tokens. The framework is lightweight and modular, enabling efficient integration into existing models without full retraining. Experimental results show that our approach reduces image tokens by up to (4x), achieves substantial gains in inference latency and training cost, and incurs only minimal performance degradation, which demonstrates the promise of token-efficient unified modeling for real world multimodal applications.
Imbalanced View Contribution Evaluation and Refinement for Deep Incomplete Multi-View Clustering
Authors: Taichun Zhou; Zhibin Dong; Hao Tan; Siwei Wang; Xinwang Liu; En Zhu; Di Hu; Tianrui Liu; Chuankun Li; Kunlun He
Venue: CVPR 2026 (main)

Abstract: In real-world applications, multi-view data often suffer from missing situations due to privacy protection and sensor failures. Such incomplete scenarios not only reduce information availability but also cause significant imbalance among views: certain "strong views" dominate the fusion process, while "weak views" contribute marginally, undermining cross-view collaboration. Existing incomplete multi-view clustering methods mainly focus on handling missing data, but overlook the view contribution imbalance induced by incompleteness and its impact on representation learning and clustering performance. To address this issue, we first analyze the data imbalance caused by missing views and the resulting disparities in view learning quality. Then, we propose an Imbalanced Contribution Evaluation and Refinement framework (ICER) for imbalanced incomplete multi-view clustering. Specifically, we employ Shapley values to quantify each view's marginal contribution, and incorporate unbalanced optimal transport to characterize distributional deviations across views. On this basis, we construct the view contribution imbalance metric to comprehensively evaluate cross-view collaboration and fusion quality, and design a collaboration enhancement module to explicitly reinforce inter-view cooperative optimization. Extensive experiments on multiple datasets demonstrate that the proposed method outperforms existing incomplete multi-view clustering approaches, validating the effectiveness and necessity of explicitly modeling and mitigating view imbalance in imbalanced incomplete scenarios. Code is released at https://github.com/Evelyn-zhou24/ICER.
MooCap: A Multi-View Benchmark for Cow-Object-Human Interaction and Behavior Dynamics
Authors: Ian Noronha; Heather Neave; Upinder Kaur
Venue: CVPR 2026 (main)

Abstract: Understanding animal behavior requires modeling how bodies, objects, and other agents interact over time, not simply detecting isolated actions or estimating pose frame by frame. Existing animal video datasets target pose estimation or coarse, passively observed actions, and rarely provide the structured, multi-entity interaction annotations needed to study behavioral dynamics. We introduce MooCap, a multi-view video benchmark for animal-object-human interaction understanding under controlled experimental protocols. MooCap contains 42 hours of synchronized multi-camera video from 43 individually tested cows across seven standardized interaction scenarios, including novel environment, novel object, novel human, human approach, unfamiliar conspecifics (restricted and unrestricted) and Dam reunion (restricted and unrestricted). Recordings are densely annotated with 23 fine-grained behaviors, 39 body keypoints across 157 test sessions, 4 spatial zones, and 43 subjects, describing interactions among subjects, objects, humans, and other cattle. We establish three benchmarks on MooCap: (1) dense temporal action segmentation over 1200-1500-second sequences; (2) pose-based behavior and interaction recognition from keypoint trajectories; and (3) longitudinal behavioral classification linking adult behaviors with rearing conditions. Benchmarking results reveal that state-of-the-art temporal segmentation models achieve only 66.4% frame accuracy and 30.6% F1@0.5, with performance degrading further during interaction-heavy segments. Overall, MooCap bridges multi-view pose estimation, multi-entity tracking, and structured behavioral protocols to enable interaction-aware models for animal behavior analysis.
InnoAds-Composer: Efficient Condition Composition for E-Commerce Poster Generation
Authors: Yuxin Qin; Ke Cao; Haowei Liu; Ao Ma; Fengheng Li; Honghe Zhu; Zheng Zhang; Run Ling; Wei Feng; Xuanhua He; Zhanjie Zhang; Zhen Guo; Haoyi Bian; Jingjing Lv; Junjie Shen; Ching Law
Venue: CVPR 2026 (main)

Abstract: E-commerce product poster generation aims to automatically synthesize a single image that effectively conveys product information by presenting a subject, text, and a designed style. Recent diffusion models with fine-grained and efficient controllability have advanced product poster synthesis, yet they typically rely on multi-stage pipelines, and simultaneous control over subject, text, and style remains underexplored. Such naive multi-stage pipelines also show three issues: poor subject fidelity, inaccurate text, and inconsistent style. To address these issues, we propose InnoAds-Composer, a single-stage framework that enables efficient tri-conditional control tokens over subject, glyph, and style. To alleviate the quadratic overhead introduced by naive tri-conditional token concatenation, we perform importance analysis over layers and timesteps and route each condition only to the most responsive positions, thereby shortening the active token sequence. Besides, to improve the accuracy of Chinese text rendering, we design a Text Feature Enhancement Module (TFEM) that integrates features from both glyph images and glyph crops. To support training and evaluation, we also construct a high-quality e-commerce product poster dataset and benchmark, which is the first dataset that jointly contains subject, text, and style conditions. Extensive experiments demonstrate that InnoAds-Composer significantly outperforms existing product poster methods without obviously increasing inference latency.
Envisioning the Future, One Step at a Time
Authors: Stefan Andreas Baumann; Jannik Wiese; Tommaso Martorella; Mahdi M. Kalayeh; Björn Ommer
Venue: CVPR 2026 (main)

Abstract: Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains, and efficiently explore many plausible futures. Yet most existing approaches rely on dense video or latent-space prediction, expending substantial capacity on dense appearance rather than on the underlying sparse trajectories of points in the scene. This makes large-scale exploration of future hypotheses costly and limits performance when long-horizon, multi-modal motion is essential. We address this by formulating the prediction of open-set future scene dynamics as step-wise inference over sparse point trajectories. Our autoregressive diffusion model advances these trajectories through short, locally predictable transitions, explicitly modeling the growth of uncertainty over time. This dynamics-centric representation enables fast rollout of thousands of diverse futures from a single image, optionally guided by initial constraints on motion, while maintaining physical plausibility and long-range coherence. We further introduce OWM, a benchmark for open-set motion prediction based on diverse in-the-wild videos, to evaluate accuracy and variability of predicted trajectory distributions under real-world uncertainty. Our method matches or surpasses dense simulators in predictive accuracy while achieving orders-of-magnitude higher sampling speed, making open-set future prediction both scalable and practical.
MeToM: Metadata-Guided Token Merging for Efficient Video LLMs
Authors: Zhuojie Wu; Shijie Wang; Xin Yu
Venue: CVPR 2026 (main)

Abstract: Video Large Language Models (VLLMs) encounter significant computational challenges due to the large volume of visual tokens generated from multiple frames. Existing visual token pruning methods fail to account for the uneven spatiotemporal information density, thus squandering scarce token budgets on regions with low information density. In this paper, we propose a training-free Metadata-guided Token Merging framework (MeToM) that leverages intrinsic video metadata to adaptively allocate budgets and merge visual tokens based on content complexity. Specifically, MeToM exploits residual data from codec metadata as spatial information density cues. It merges less informative regions during tokenization, avoiding redundant encoding and improving the efficiency of the visual encoder. Additionally, MeToM captures temporal variations in information density by utilizing the average Group of Pictures (GoP) packet size to represent scene complexity. This mechanism enables dynamic per-frame token allocation across time, assigning more tokens to content-complex frames and fewer tokens to information-sparse ones. Finally, we merge low-contribution visual tokens via multi-layer attention to reduce prefill FLOPs and the visual KV-cache footprint inside the LLM. Extensive experimental results demonstrate that MeToM outperforms prior state-of-the-art methods.  Notably, it achieves a 2.65xinference speedup over the baseline VLLM without sacrificing accuracy.
Breaking Spurious Correlations: Uncertainty-Driven Causal Transformers for AU Detection
Authors: Yuru Wang; Yue Zhou
Venue: CVPR 2026 (main)

Abstract: Facial Action Unit (AU) detection suffers from limited annotated data, severe class imbalance, label noise, and confounding biases, which often lead to overfitting and degraded performance. We propose an Uncertainty-Driven Causal Transformer (UDCT) framework for robust AU detection by jointly modeling uncertainty and causal intervention. Specifically, we parameterize Transformer attention weights as Gaussian distributions to capture robust AU dependencies while explicitly modeling uncertainty in attention. We further introduce an uncertainty-aware loss reweighting strategy to alleviate the effects of class imbalance and label noise during training. In addition, we incorporate a causal intervention module to suppress confounder-dependent AU associations and encourage the model to focus on more stable and less biased AU relationships. Experiments on BP4D and DISFA demonstrate that UDCT achieves competitive performance with stronger robustness under noisy, imbalanced, and distribution-shifted settings.
Progressive Multi-cue Alignment for Unaligned RGBT Tracking
Authors: Jiandong Jin; Chenglong Li; Hao Feng; Andong Lu; Lili Huang; Jin Tang
Venue: CVPR 2026 (main)

Abstract: Unaligned RGBT tracking aims to achieve robust target localization across spatially misaligned RGB and thermal infrared (TIR) videos, a crucial challenge for deploying RGBT tracking in real-world scenarios. Existing methods often calculate all cross-modal alignment parameters (i.e., spatial shift and scale change) simultaneously, but suffer from two major limitations. 1) They are difficult to adapt to different degrees of unaligned difficulty during tracking. 2) They usually require complex models to handle challenging scenarios, resulting in a large computational burden. To overcome these limitations, we propose a novel Progressive Multi-cue Alignment framework called PMATrack, which disentangles the calculation of cross-modal alignment parameters in a progressive manner and dynamically selects appropriate cues to handle different challenges, thereby enabling robust and efficient unaligned RGBT tracking.  In particular, PMATrack divides the cross-modal alignment parameter estimation into three stages to progressively perform center offset computation, scale transformation estimation, and global refinement. At each stage, we design a difficulty-aware router to adaptively select the appropriate alignment expert based on the cross-modal alignment complexity, thereby reducing computational redundancy. In addition, we build a high-quality video benchmark called MUART244 to facilitate the comprehensive evaluation of different unaligned RGBT tracking algorithms. Extensive experiments demonstrate the outstanding performance of PMATrack against existing state-of-the-art methods. The code and dataset will be available at https://github.com/NOP1224/Unaligned_RGBT_Tracking.
DynamicVGGT: Learning Dynamic Point Maps for 4D Scene Reconstruction in Autonomous Driving
Authors: Zhuolin He; Jing Li; Guanghao Li; Xiaolei Chen; Jiacheng Tang; Siyang Zhang; Zhounan Jin; Feipeng Cai; Bin Li; Jian Pu; Jia Cai; Xiangyang Xue
Venue: CVPR 2026 (main)

Abstract: Dynamic scene reconstruction in autonomous driving remains a fundamental challenge due to significant temporal variations, moving objects, and complex scene dynamics. Existing feed-forward 3D models have demonstrated strong performance in static reconstruction but still struggle to capture dynamic motion. To address these limitations, we propose DynamicVGGT, a unified feed-forward framework that extends VGGT from static 3D perception to dynamic 4D reconstruction. Our goal is to model point motion within feed-forward 3D models in a dynamic and temporally coherent manner. To this end, we jointly predict the current and future point maps within a shared reference coordinate system, allowing the model to implicitly learn dynamic point representations through temporal correspondence. To efficiently capture temporal dependencies, we introduce a Motion-aware Temporal Attention (MTA) module that learns motion continuity. Furthermore, we design a Dynamic 3D Gaussian Splatting Head (DGSH) that explicitly models point motion by predicting Gaussian velocities using learnable motion tokens under scene flow supervision. It refines dynamic geometry through continuous 3D Gaussian optimization. Extensive experiments on autonomous driving datasets demonstrate that DynamicVGGT significantly outperforms existing methods in reconstruction accuracy and temporal consistency, achieving robust feed-forward 4D dynamic scene reconstruction under complex driving scenarios.
Self-Evaluation Unlocks Any-Step Text-to-Image Generation
Authors: Xin Yu; Xiaojuan Qi; Zhengqi Li; Kai Zhang; Richard Zhang; Zhe Lin; Eli Shechtman; Tianyu Wang; Yotam Nitzan
Venue: CVPR 2026 (main)

Abstract: We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data similarly to a Flow Matching model, while simultaneously employing a novel self-evaluation mechanism: it evaluates its own generated samples using its current score estimates, effectively serving as a dynamic self-teacher. Unlike traditional diffusion or flow models, it does not rely solely on local supervision, which typically necessitates many inference steps. Unlike distillation-based approaches, it does not require a pretrained teacher. This combination of instantaneous local learning and self-driven global matching bridges the gap between the two paradigms, enabling the training of a high-quality text-to-image model from scratch that excels even at very low step counts. Extensive experiments on large-scale text-to-image benchmarks show that Self-E not only excels in few-step generation, but is also competitive with state-of-the-art Flow Matching models at 50 steps. We further find that its performance improves monotonically as inference steps increase, enabling both ultra-fast few-step generation and high-quality long-trajectory sampling within a single unified model. To our knowledge, Self-E is the first from-scratch, any-step text-to-image model, offering a unified framework for efficient and scalable generation.
AURA: Multi-modal Shared Autonomy for Urban Navigation
Authors: Yukai Ma; Honglin He; Selina Song; Wayne Wu; Bolei Zhou
Venue: CVPR 2026 (main)

Abstract: Long-horizon navigation in complex urban environments relies heavily on continuous human operation, which leads to fatigue, reduced efficiency, and safety concerns. Shared autonomy, where a Vision-Language AI agent and a human operator collaborate on maneuvering the mobile machine, presents a promising solution to address these issues. However, existing shared autonomy methods often require humans and AI to operate within the same action space, leading to high cognitive overhead. We present Assistive Urban Robot Autonomy (AURA), a new multi-modal framework that decomposes urban navigation into high-level human instruction and low-level AI control. AURA incorporates a Spatial-Aware Instruction Encoder to align various human instructions with visual and spatial context. To facilitate training, we construct MM-CoS, a large-scale dataset comprising teleoperation and vision-language descriptions. Experiments in simulation and the real world demonstrate that AURA effectively follows human instructions, reduces manual operation effort, and improves navigation stability, while enabling online adaptation. Moreover, under similar takeover conditions, our shared autonomy framework reduces the frequency of takeovers by more than 44%. Demo video and more detail are provided in the project page.
LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes
Authors: Ruofan Liang; Norman Müller; Ethan Weber; Duncan Zauss; Nandita Vijaykumar; Peter Kontschieder; Christian Richardt
Venue: CVPR 2026 (main)

Abstract: We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative image-based light decomposition model that factorizes complex indoor scene illumination into its constituent light sources. This factorization enables independent manipulation of individual light sources, specifically allowing control over their state (on/off), chromaticity, and intensity. We further introduce multi-view lighting harmonization to ensure consistent propagation of the lighting decomposition across all scene views. This is integrated into a relightable 3D Gaussian splatting representation, providing real-time interactive control over the individual light sources. Our results demonstrate highly photorealistic lighting decomposition and relighting outcomes across diverse indoor scenes. We evaluate our method on both synthetic and real-world datasets and provide a quantitative and qualitative comparison to state-of-the-art techniques.
AVATAR: Reinforcement Learning to See, Hear, and Reason Over Video
Authors: Yogesh Kulkarni; Pooyan Fazli
Venue: CVPR 2026 (main)

Abstract: Multimodal reasoning over long-horizon video is challenging due to the need for precise spatiotemporal fusion and alignment across modalities. While recent methods such as Group Relative Policy Optimization (GRPO) have shown promise in this domain, they suffer from three key limitations: (1) data inefficiency from their on-policy design, (2) a vanishing advantage problem, where identical or near-identical rewards within a group eliminate the learning signal by producing zero-valued advantages, and (3) uniform credit assignment that fails to emphasize critical reasoning steps. We introduce AVATAR (Audio-Video Agent for Alignment and Reasoning, a framework that addresses these limitations through two core components: (1) an off-policy training architecture that improves sample efficiency and resolves vanishing advantages by reusing past experiences with greater reward diversity, and (2) Temporal Advantage Shaping (TAS), a credit assignment strategy that emphasizes early (planning) and late (synthesis) reasoning phases. AVATAR achieves strong performance across various benchmarks, outperforming the Qwen2.5-Omni baseline by +5.4 on MMVU, +4.9 on OmniBench, and +4.5 on Video-Holmes. Furthermore, it surpasses standard GRPO by +3.7 on OmniBench and +1.9 on Video-Holmes, while demonstrating 5 times sample efficiency, requiring 80 fewer generated completions to reach target performance.
Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion
Authors: Yueming Pan; Ruoyu Feng; Qi Dai; Yuqi Wang; Wenfeng Lin; Mingyu Guo; Chong Luo; Nanning Zheng
Venue: CVPR 2026 (main)

Abstract: Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. This indicates the preceding semantics potentially benefit the texture generation by providing a semantic anchor. Recent advances have integrated semantic priors from pretrained visual encoders to further enhance LDMs, yet they still denoise semantic and VAE-encoded texture synchronously, neglecting such ordering. Observing these, we propose Semantic-First Diffusion (SFD), a latent diffusion paradigm that explicitly prioritizes semantic formation. SFD first constructs composite latents by combining the compact semantic latent, which is extracted from pretrained visual encoder via a dedicated Semantic VAE, with the texture latent. The core of SFD is to denoise the semantic and texture latents asynchronously using separate noise schedules: semantics precede textures by a temporal offset, providing clearer high-level guidance for texture refinement and enabling natural coarse-to-fine generation. On ImageNet 256x256 with guidance, SFD achieves FID 1.06 (LightningDiT-XL) and FID 1.04 (1.0B LightningDiT-XXL), while achieving up to 100xfaster convergence than original DiT without guidance. SFD also improves existing methods like ReDi and VA-VAE, demonstrating the effectiveness of asynchronous, semantics-led modeling.
Geometry-Aligned and Anomaly-Aware Reconstruction for 3D Anomaly Detection
Authors: Linchun Wu; Qin Zou; Yuanhao Yue; Zhongyuan Wang
Venue: CVPR 2026 (main)

Abstract: Point cloud anomaly detection is crucial in automated manufacturing, with reconstruction-based diffusion methods emerging as a mainstream solution. However, these approaches still face two major challenges: (1) geometry violation, where random noise perturbations deviate from local surface normals, causing structural distortion; and (2) undistinguished reference regions, where uniformly applied coarse anomaly embeddings during denoising blur normal details and impede accurate anomaly recovery. To address these issues, we propose AARD, a geometry-aligned and anomaly-aware diffusion reconstruction framework. We argue that high-fidelity anomaly detection requires a principled reformulation of the diffusion process: noise should align with geometry to preserve structures, and reconstruction can be better guided by anomaly-aware references to discriminatively recover normal details while correcting defects. AARD progressively aligns noise directions with vertex normals while maintaining vertex-graph consistency, and employs an adaptive transformer that assigns normal references to anomalous regions and input references to normal areas. Experiments on Anomaly-ShapeNet and Real3D-AD show that AARD consistently outperforms state-of-the-art approaches, achieving superior geometric fidelity and robust anomaly localization.