GenAI4ELab/papercli-papers huggingface dataset:
Towards Dynamic Modality Alignment in Multimodal Continual Learning
Authors: Jiayao Tan; Fan Lyu; Tianle Liu; Fuyuan Hu; Wei Feng
Venue: CVPR 2026 (main)

Abstract: Multimodal Continual Learning (MMCL) aims to enable models to continuously accumulate knowledge across multiple tasks and modalities without forgetting prior information. MMCL presents more challenges than single-modal continual learning, as it requires effective cooperation and complementarity between modalities. Existing methods often treat modality alignment as a static process, assuming once alignment is established, it remains fixed. However, we argue that modality alignment is inherently dynamic, evolving with task learning and feature propagation across layers. To address this, we introduce Dynamic Alignment Graph Regularization (DAGR), a novel approach that explicitly models the evolving alignment across layers. By incorporating multi-level graph regularization, our method stabilizes the alignment process and mitigates catastrophic forgetting. Extensive experiments on benchmarks, such as MTIL, show that DAGR outperforms static alignment-based methods and other continual learning techniques, achieving superior stability.
Test-Time Perturbation Tuning with Delayed Feedback for Vision-Language-Action Models
Authors: Zehua Zang; Xi Wang; Fuchun Sun; Xiao Xu; Lixiang Liu; Jiahuan Zhou; Jiangmeng Li
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action models (VLAs) achieve remarkable performance in sequential decision-making but remain fragile to subtle environmental shifts, such as small changes in object pose. We attribute this brittleness to trajectory overfitting, where VLAs over-attend to the spurious correlation between actions and entities, then reproduce memorized action patterns. We propose Perturbation learning with Delayed Feedback (PDF), a verifier-free test-time adaptation framework that improves decision performance without fine-tuning the base model. PDF mitigates the spurious correlation through uncertainty-based data augmentation and action voting, while an adaptive scheduler allocates augmentation budgets to balance performance and efficiency. To further improve stability, PDF learns a lightweight perturbation module that retrospectively adjusts action logits guided by delayed feedback, correcting overconfidence issue. Experiments on LIBERO (+7.4% success rate) and Atari (+10.3 human normalized score) demonstrate consistent gains of PDF in task success over vanilla VLA and VLA with test-time adaptation, establishing a practical path toward reliable test-time adaptation in multimodal decision-making agents.
SonoWorld: From One Image to a 3D Audio-Visual Scene
Authors: Derong Jin; Xiyi Chen; Ming C. Lin; Ruohan Gao
Venue: CVPR 2026 (main)

Abstract: Tremendous progress in visual scene generation now turns a single image into an explorable 3D world, yet immersion remains incomplete without sound. We introduce Image2AVScene, the task of generating a 3D audio-visual scene from a single image, and present SonoWorld, the first framework to tackle this challenge. From one image, our pipeline outpaints a 360deg panorama, lifts it into a navigable 3D scene, places language-guided sound anchors, and renders ambisonics for point, areal, and ambient sources, yielding spatial audio aligned with scene geometry and semantics. Quantitative evaluations on a newly curated real-world dataset and a controlled user study confirm the effectiveness of our approach. Beyond free-viewpoint audio-visual rendering, we also demonstrate applications to one-shot acoustic learning and audio-visual spatial source separation. Project website: https://humathe.github.io/sonoworld/
AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance
Authors: Tianling Xu; Shengzhe Gan; Leslie Gu; Yuelei Li; Fangneng Zhan; Hanspeter Pfister
Venue: CVPR 2026 (main)

Abstract: Active 3D reconstruction enables an agent to autonomously select viewpoints to build accurate and complete scene geometry efficiently, rather than passively reconstructing scenes from pre-collected images. Existing active reconstruction methods often rely on geometric heuristics, which may result in redundant observations without improving reconstruction quality. To address this, we propose AREA3D, an active reconstruction agent for 3D reconstruction by leveraging feed-forward 3D models and vision-language guidance. The framework decouples view uncertainty modeling from feed-forward reconstruction, enabling precise uncertainty estimation without online optimization. Moreover, the integrated Vision-Language Model provides high-level semantic guidance that guides exploration beyond purely geometric cues. Extensive experiments on both scene-level and object-level benchmarks (Replica and OmniObject3D) demonstrate that AREA3D achieves state-of-the-art reconstruction accuracy, especially in sparse views.
Towards High-resolution and Disentangled Reference-based Sketch Colorization
Authors: Dingkun Yan; Xinrui Wang; Ru Wang; Zhuoru Li; Jinze Yu; Yusuke Iwasawa; Yutaka Matsuo; Jiaxian Guo
Venue: CVPR 2026 (main)

Abstract: Sketch colorization models have been widely studied to automate and assist in the creation of animation frames and digital illustrations. However, current methods are still not satisfactory for industrial standard applications in high-resolution synthesis and precise controllability of details. To further enhance the synthesis quality and controllability, we propose an image-referenced sketch colorization method based on the powerful SDXL backbone and leverage sketches as spatial guidance and RGB images as color references. A split cross-attention mechanism is coupled with spatial masks to separately colorize the foreground and background regions to avoid spatial entanglement. A tagger network trained on a massive anime-style image dataset is employed to extract attribution-level information from reference images and integrated into the pipeline to provide precise control signals for synthesis. However, the increased resolution and number of attention layers in the SDXL backbone and precise reference information from the tagger network cause severe entanglement during colorization. We consequently combine a foreground encoder and a background encoder for disentanglement and better synthesis quality. Furthermore, a high-quality annotated and paired sketch colorization dataset is collected for fine-tuning. The proposed method is the first to achieve high resolution high quality sketch colorization with precise control, and obvious outperforms existing methods in quantitative and qualitative validations, as well as user studies in both quality and controllability. Ablation study reveals the influence of each component. Code and dataset will be made publicly available upon paper acceptance.
Detect Any AI-Counterfeited Text Image
Authors: Chenfan Qu; Yiwu Zhong; Xuekang Zhu; Junchi Li; Changjiang Jiang; Jian liu; Lianwen Jin
Venue: CVPR 2026 (main)

Abstract: The rapid advancement of generative AI enables the creation of highly realistic text images, posing significant security risks from fraud and disinformation. However, research into robust detection is critically hampered by existing datasets that lack scale, diversity, and updated counterfeit techniques, as well as by models that fail to generalize. To address these deficiencies, we introduce DanceText, a large-scale, comprehensive dataset for AI-counterfeited text image detection. Constructed using our novel Creative Proposer pipeline, which automates the generation of diverse and realistic counterfeits, DanceText surpasses previous works by over 100-fold in multiple dimensions. It is the first to include counterfeits from multimodal large models, commercial software, and mobile apps, covering all major paradigms, including full-image generation, regional removal, and editing. Building on this dataset, we propose DS-Net, a novel and effective detection model. It features two key components: a Forensic Decoupling Encoder to extract generator-agnostic artifact features, and a Synergy Denoising Decoder that synergizes image-level classification with instance-level localization. Extensive experiments demonstrate that DS-Net achieves state-of-the-art performance, advancing the field toward robust and unified detection of AI-counterfeited text images in real-world scenarios. Both our code and dataset will be released publicly.
ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction
Authors: Aoyu Liu; Zhen Liu; Ziyi Wang; Dian Chen; Bing Zeng; Shuaicheng Liu
Venue: CVPR 2026 (main)

Abstract: Single-image HDR reconstruction aims to recover high dynamic range radiance from a single low dynamic range (LDR) input, but remains highly ill-posed due to detail saturation in over-exposed regions and noise amplification in under-exposed areas. While recent diffusion-based approaches offer powerful generative priors, they often overlook the exposure-dependent nature of the degradation and incur substantial computational costs from iterative sampling. To address these challenges, we propose ExpoCM, a novel one-step generative HDR reconstruction framework that reformulates HDR reconstruction as a Probability Flow ODE (PF-ODE) and constructs exposure-aware consistency trajectories via exposure-dependent perturbations. Specifically, a soft exposure mask is first constructed to separate the LDR image into over-, under-, and well-exposed regions. Based on this partition, region-conditioned consistency trajectories are designed to hallucinate saturated details, suppress noise in dark regions, and preserve reliable structures within a single, distillation-free inference step. To further enhance perceptual quality, we introduce an Exposure-guided Luminance-Chromaticity Loss in the CIE \text L ^*\text a ^*\text b ^* space, which assigns exposure-aware weights to luminance and chromaticity components, effectively mitigating brightness bias and color drift. Extensive experiments on the HDR-REAL, HDR-EYE, and AIM2025 benchmarks demonstrate that ExpoCM achieves state-of-the-art fidelity and perceptual accuracy, while enabling over 400xand 20xfaster inference compared to DDPM (1000 steps) and DDIM (50 steps), respectively. Code is available at https://github.com/AoyuLiu01/ExpoCM.
UniDex: A Robot Foundation Suite for Universal Dexterous Hand Control from Egocentric Human Videos
Authors: Gu Zhang; Qicheng Xu; Haozhe Zhang; Jianhan Ma; Long He; Yiming Bao; Zeyu Ping; Zhecheng Yuan; Chenhao Lu; Chengbo Yuan; Tianhai Liang; Xiaoyu Tian; Maanping Shao; Feihong Zhang; Mingyu Ding; Yang Gao; Hao Zhao; Hang Zhao; Huazhe Xu
Venue: CVPR 2026 (main)

Abstract: Dexterous manipulation remains challenging due to the cost of collecting real-robot teleoperation data, the heterogeneity of hand embodiments, and the high dimensionality of control. We present UniDex, a robot foundation suite that couples a large-scale robot-centric dataset with a unified vision-language-action (VLA) policy and a practical human-data capture setup for universal dexterous hand control. First, we construct UniDex-Dataset, a robot-centric dataset over 50K trajectories across eight dexterous hands (6-24 DoFs), derived from egocentric human video datasets. To transform human data into robot-executable trajectories, we employ a human-in-the-loop retargeting procedure to align fingertip trajectories while preserving plausible hand-object contacts, and we operate on explicit 3D pointclouds with human hands masked to narrow kinematic and visual gaps. Second, we introduce the Function-Actuator-Aligned Space (FAAS), a unified action space that maps functionally similar actuators to shared coordinates, enabling cross-hand transfer. Leveraging FAAS as the action parameterization, we train UniDex-VLA, a 3D VLA policy pretrained on UniDex-Dataset and finetuned with task demonstrations. In addition, we build UniDex-Cap, a simple portable capture setup that records synchronized RGB-D streams and human hand poses and converts them into robot-executable trajectories to enable human-robot data co-training that reduces reliance on costly robot demonstrations. On challenging tool-use tasks across two different hands, UniDex-VLA achieves 81% average task progress and outperforms prior VLA baselines by a large margin, while exhibiting strong spatial, object, and zero-shot cross-hand generalization. Together, UniDex-Dataset, UniDex-VLA, and UniDex-Cap provide a scalable foundation suite for universal dexterous manipulation.
Open-world Hand-Object Interaction Video Generation Based on Structure and Contact-aware Representation
Authors: Haodong Yan; Hang Yu; Zhide Zhong; Weilin Yuan; Xin Gong; Zehang Luo; Chengxi Heyu; Junfeng Li; Wenxuan Song; Shunbo Zhou; Haoang Li
Venue: CVPR 2026 (main)

Abstract: Generating realistic hand-object interactions (HOI) videos is a significant challenge due to the difficulty of modeling physical constraints (e.g., contact and occlusion between hands and manipulated objects). Current methods utilize HOI representation as an auxiliary generative objective to guide video synthesis. However, there is a dilemma between 2D and 3D representations that cannot simultaneously guarantee scalability and interaction fidelity. To address this limitation, we propose a structure and contact-aware representation that captures hand-object contact, hand-object occlusion, and holistic structure context without 3D annotations. This interaction-oriented and scalable supervision signal enables the model to learn fine-grained interaction physics and generalize to open-world scenarios. To fully exploit the proposed representation, we introduce a joint-generation paradigm with a share-and-specialization strategy that generates interaction-oriented representations and videos. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two real-world datasets in generating physics-realistic and temporally coherent HOI videos. Furthermore, our approach exhibits strong generalization to challenging open-world scenarios, highlighting the benefit of our scalable design.
LaMoGen: Language to Motion Generation Through LLM-Guided Symbolic Inference
Authors: Junkun Jiang; Ho Yin Au; Jingyu Xiang; Jie Chen
Venue: CVPR 2026 (main)

Abstract: Human motion is highly expressive and naturally aligned with language, yet prevailing methods relying heavily on joint text-motion embeddings struggle to synthesize temporally accurate, detailed motions and often lack explainability. To address these limitations, we introduce LabanLite, a motion representation developed by adapting and extending the Labanotation system. Unlike black-box text-motion embeddings, LabanLite encodes each atomic body-part action (e.g., a single left-foot step) as a discrete Laban symbol paired with a textual template. This abstraction decomposes complex motions into interpretable symbol sequences and body-part instructions, establishing a symbolic link between high-level language and low-level motion trajectories. Building on LabanLite, we present LaMoGen, a Text-to-LabanLite-to-Motion Generation framework that enables large language models (LLMs) to compose motion sequences through symbolic reasoning. The LLM interprets motion patterns, relates them to textual descriptions, and recombines symbols into executable plans, producing motions that are both interpretable and linguistically grounded. To support rigorous evaluation, we introduce a Labanotation-based benchmark with structured description-motion pairs and three metrics that jointly measure text-motion alignment across symbolic, temporal, and harmony dimensions. Experiments demonstrate that LaMoGen establishes a new baseline for both interpretability and controllability, outperforming prior methods on our benchmark and two public datasets. These results highlight the advantages of symbolic reasoning and agent-based design for language-driven motion synthesis.
DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution
Authors: Shuhao Han; Wenjie Liao; Hayden Vance; Hang Dong; Rui Zhang; Chun-Le Guo; Chongyi Li
Venue: CVPR 2026 (main)

Abstract: Benefiting from the powerful generative priors of diffusion models, diffusion-based real-world image super-resolution (Real-ISR) methods have demonstrated impressive performance.To achieve efficient Real-ISR, several recent works have designed one-step diffusion-based models.Howerver, unmediatedly feeding LR into a diffusion model creates a distributional gap with the model's original input.A straightforward approach to reduce the distribution gap is to introduce noise to the LR latents. However, directly adding noise inevitably corrupts the content of the LR images.In this study, we propose DNF-SR, a Dual-input and Negative-aware Feature fine-tuning method for Real-ISR.Specifically, we use a dual-input strategy that concatenates the original LR image with the noisy LR input and feeds them into a diffusion-based image editing model, ensuring both high-fidelity one-step super-resolution and improved perceptual and content consistency.Additionally, the noise present in the noisy LR input introduces randomness and diversity into the outputs. We exploit this property and propose a post-training optimization method, Negative-aware Feature Fine-Tuning (NF2T), which guides the model toward producing higher-quality results.NF^2T classifies multiple outputs into positive and negative subsets and then defines implicit policy improvement directions in both the image and feature spaces, thereby further enhancing the stability of the optimization.Extensive experiments show that DNF-SR outperforms other methods.Code will be released.
Unifying Language-Action Understanding and Generation for Autonomous Driving
Authors: Xinyang Wang; Qian Liu; Wenjie Ding; Zhao Yang; Wei Li; Chang Liu; Bailin Li; Kun Zhan; Xianpeng Lang; Wei Chen
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action (VLA) models are emerging as a promising paradigm for end-to-end autonomous driving, valued for their potential to leverage world knowledge and reason about complex driving scenes. However, existing methods suffer from two critical limitations: a persistent misalignment between language instructions and action outputs, and the inherent inefficiency of typical auto-regressive action generation. In this paper, we introduce LinkVLA, a novel architecture that directly addresses these challenges to enhance both alignment and efficiency. First, we establish a structural link by unifying language and action tokens into a shared discrete codebook, processed within a single multi-modal model. This structurally enforces cross-modal consistency from the ground up. Second, to create a deep semantic link, we introduce an auxiliary action understanding objective that trains the model to generate descriptive captions from trajectories, fostering a bidirectional language-action mapping. Finally, we replace the slow, step-by-step generation with a two-step coarse-to-fine generation method (C2F) that efficiently decodes the action sequence, saving 86% inference time. Experiments on closed-loop driving benchmarks show consistent gains in instruction following accuracy and driving performance, alongside reduced inference latency.
Tackling Model Bias via Game-theoretic Multi-agent Collaboration Framework for Hateful Meme Classification
Authors: Yiwei Wei; Zhengliang Guo; Shaozu Yuan; Chengyin Hu; Zhiyang Jia; Jiujiang Guo; Meng Chen; Peiying Wang; Longbiao Wang
Venue: CVPR 2026 (main)

Abstract: Hateful meme classification aims to identify memes containing hateful content and has become increasingly important in the era of social media dominance. Large multimodal models (LMMs) have significantly enhanced the understanding of multimodal content, advancing this field. However, cognitive biases in LMMs can impede effective collaboration among models. To address this issue, we introduce GECO, a Game-theoretic multi-agEnt Collaboration framewOrk that organizes multiple LMMs into interacting agents and employs game-theoretic principles to guide them toward an optimal cooperative equilibrium. GECO integrates a mixed bonus scheme, incorporating both individual accuracy and cross-model agreement to promote convergence toward a consistent cooperative solution. In addition, we implement efficient policy learning and introduce a penalty coefficient to optimize the framework effectively and ensure training stability. Extensive experiments on five publicly available benchmarks demonstrate that our framework achieves new state-of-the-art performance.
ProFocus: Proactive Perception and Focused Reasoning in Vision-and-Language Navigation
Authors: Wei Xue; Mingcheng Li; Xuecheng Wu; Jingqun Tang; Dingkang Yang; Lihua Zhang
Venue: CVPR 2026 (main)

Abstract: Vision-and-Language Navigation (VLN) requires agents to accurately perceive complex visual environments and reason over navigation instructions and histories. However, existing methods passively process redundant visual inputs and treat all historical contexts indiscriminately, resulting in inefficient perception and unfocused reasoning. To address these challenges, we propose ProFocus, a training-free progressive framework that unifies Proactive Perception and Focused Reasoning through collaboration between large language models (LLMs) and vision-language models (VLMs). For proactive perception, ProFocus transforms panoramic observations into structured ego-centric semantic maps, enabling the orchestration agent to identify missing visual information needed for reliable decision-making, and to generate targeted visual queries with corresponding focus regions that guide the perception agent to acquire the required observations. For focused reasoning, we propose Branch-Diverse Monte Carlo Tree Search (BD-MCTS) to identify top-k high-value waypoints from extensive historical candidates. The decision agent focuses reasoning on the historical contexts associated with these waypoints, rather than considering all historical waypoints equally. Extensive experiments validate the effectiveness of ProFocus, achieving state-of-the-art performance among zero-shot methods on R2R and REVERIE benchmarks.
Taming Generative Diffusion Model for Task-Oriented Infrared Imaging
Authors: Tengyu Ma; Zhilong Dai; Yubo Diao; Guanming An; Long Ma; Jinyuan Liu; Risheng Liu
Venue: CVPR 2026 (main)

Abstract: Infrared imaging is essential for perception in harsh environments. However, dynamically coupled degradation factors severely impair visual quality and downstream semantic accuracy. Although generative diffusion models provide strong image restoration priors, high computational cost and physical inconsistency limit their application in infrared sensing. To bridge these gaps, we reformulate infrared imaging as a single-step diffusion process, aligning degraded observations with trajectory latent states via dynamic timestep estimation to leverage timestep-specific diffusion priors for high-fidelity reconstruction. Meanwhile, we introduce a spectral regularization term to enforce thermal radiation constraints and ensure physical consistency. Subsequently, a task-aware low-rank adaptation mechanism is devised through dynamic prompting to enable efficient transfer across downstream infrared tasks. Experiments demonstrate our method surpasses existing approaches in restoration quality, semantic structure preservation, and task generalization. The code is available at https://github.com/csmty/InfraredIR.