GenAI4ELab/papercli-papers huggingface dataset:
Cross-Hand Latent Representation for Vision-Language-Action Models
Authors: Guangqi Jiang; Yutong Liang; Jianglong Ye; Jia-Yang Huang; Changwei Jing; Rocky Duan; Pieter Abbeel; Xiaolong Wang; Xueyan Zou
Venue: CVPR 2026 (main)

Abstract: Dexterous manipulation is essential for real-world robot autonomy, mirroring the central role of human hand coordination in daily activity. Humans rely on rich multimodal perception--vision, sound, and language-guided intent--to perform dexterous actions, motivating vision-based, language-conditioned manipulation systems for robots. However, training reliable vision-language-action (VLA) models for dexterous manipulation requires large-scale demonstrations across many robotic hands. In addition, as new dexterous embodiments appear rapidly, collecting data for each becomes costly and impractical, creating a need for scalable cross-embodiment learning. We introduce \ourmethod, a vision-language-action framework integrated with a unified latent action space shared across diverse dexterous hands. This embodiment-invariant latent space is directly pluggable into standard VLA architectures, enabling seamless cross-embodiment training and efficient reuse of both existing and newly collected data. Experimental results demonstrate that \ourmethod consistently outperforms baseline VLA models operating in raw joint spaces, establishing it as an effective solution for scalable cross-embodiment dexterous manipulation.
NoOVD: Novel Category Discovery and Embedding for Open-Vocabulary Object Detection
Authors: Yupeng Zhang; Ruize Han; Zhiwei Chen; Wei Feng; Liang Wan
Venue: CVPR 2026 (main)

Abstract: Despite the remarkable progress in open-vocabulary object detection (OVD), a significant gap remains between the training and testing phases. During training, the RPN and RoI heads often misclassify unlabeled novel-category objects as background, causing some proposals to be prematurely filtered out by the RPN while others are further misclassified by the RoI head. During testing, these proposals again receive low scores and are removed in post-processing, leading to a significant drop in recall and ultimately weakening novel-category detection performance.To address these issues, we propose a novel training framework--NoOVD--which innovatively integrates a self-distillation mechanism grounded in the knowledge of frozen vision-language models (VLMs).Specifically, we design K-FPN, which leverages the pretrained knowledge of VLMs to guide the model in discovering novel-category objects and facilitates knowledge distillation--without requiring additional data--thus preventing forced alignment of novel objects with background.Additionally, we introduce R-RPN, which adjusts the confidence scores of proposals during inference to improve the recall of novel-category objects.Cross-dataset evaluations on OV-LVIS, OV-COCO, and Objects365 demonstrate that our approach consistently achieves superior performance across multiple metrics.
VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset
Authors: Zhizhou Chen; Shanyan Guan; Zhanxin Gao; En Ci; Yanhao Ge; Wei Li; Zhenyu Zhang; Jian Yang; Ying Tai
Venue: CVPR 2026 (main)

Abstract: Directly editing ultra-high-resolution (UHR) images is valuable but underexplored, primarily due to the lack of high-quality data and the challenge in modeling high-frequency texture details. We introduce VINS-120K, the first large-scale dataset for instruction-based UHR image editing, comprising 120K carefully curated triplets of instruction, input image, and edited image. Each image exceeds 4K resolution (\geq4096x4096) and is filtered through a rigorous multi-stage pipeline to ensure visual quality, instruction alignment, and aesthetic fidelity. Built on VINS-120K, we further develop a high-frequency-aware post-adaptation strategy to extend pretrained non-high-resolution models to the UHR regime. We also present VINS-4KEval, a benchmark covering diverse editing types, to facilitate consistent evaluation in UHR settings. Experiments confirm that our work improves fine-grained detail synthesis and texture realism in UHR image editing.
SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving
Authors: Felix Embacher; Jonas Uhrig; Marius Cordts; Markus Enzweiler
Venue: CVPR 2026 (main)

Abstract: Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant samples. We introduce SearchAD, a large-scale rare image retrieval dataset for AD containing over 423k frames drawn from 11 established datasets. SearchAD provides high-quality manual annotations of more than 513k bounding boxes covering 90 rare categories. It specifically targets the "needle-in-a-haystack" problem of locating extremely rare classes, with some appearing fewer than 50 times across the entire dataset. Unlike existing benchmarks, which focused on instance-level retrieval, SearchAD emphasizes semantic image retrieval with a well-defined data split, enabling text-to-image and image-to-image retrieval, few-shot learning, and fine-tuning of multi-modal retrieval models. Comprehensive evaluations show that text-based methods outperform image-based ones due to stronger inherent semantic grounding. While models directly aligning spatial visual features with language achieve the best zero-shot results, and our fine-tuning baseline significantly improves performance, absolute retrieval capabilities remain unsatisfactory. With a held-out test set on a public benchmark server, SearchAD establishes the first large-scale dataset for retrieval-driven data curation and long-tail perception research in AD: https://iis-esslingen.github.io/searchad/
Space-Time Forecasting of Dynamic Scenes with Motion-aware Gaussian Grouping
Authors: Junmyeong Lee; Hoseung Choi; Minsu Cho
Venue: CVPR 2026 (main)

Abstract: Forecasting dynamic scenes remains a fundamental challenge in computer vision, as limited observations make it difficult to capture coherent object-level motion and long-term temporal evolution.We present Motion Group-aware Gaussian Forecasting (MoGaF), a framework for long-term scene extrapolation built upon the 4D Gaussian Splatting representation.MoGaF introduces motion-aware Gaussian grouping and group-wise optimization to enforce physically consistent motion across both rigid and non-rigid regions, yielding spatially coherent dynamic representations.Leveraging this structured space-time representation, a lightweight forecasting module predicts future motion, enabling realistic and temporally stable scene evolution.Experiments on synthetic and real-world datasets demonstrate that MoGaF consistently outperforms existing baselines in rendering quality, motion plausibility, and long-term forecasting stability.
POGA: Paraphrased and Oppositional Graph Alignment for Fine-Grained Cross-Modal Retrieval
Authors: Junfeng Zhang; Zhe Xue; Yuankai Qi; Junping Du; Xiangyang Kong; Yishuo Yan; Amin Beheshti; Jian Yang; Anton van den Hengel; Ming-Hsuan Yang
Venue: CVPR 2026 (main)

Abstract: Most of the models used to generate embeddings for retrieval are not trained for the purpose which leads them to focus on coarse semantic alignment rather than particular object attributes or arrangements. This limits their performance, particularly on challenging problems such as cross-modal fine-grained retrieval. Furthermore, their training objectives lack the discriminative ability required to distinguish between descriptions that are semantically similar but factually different. To address these challenges, we propose POGA (Paraphrased and Oppositional Graph Alignment), a novel framework for fine-grained cross-modal alignment. POGA comprises two core innovations: (1) Multi-source Graph Augmentation (MSGA), which not only generates paraphrased positives and oppositional negatives, but also parses the image and all text variants into structured graphs to provide difference-rich supervisory signals; (2) Hybrid Multi-granularity Alignment (HMA), which defines a composite training objective that jointly optimizes the model at four distinct granularities: including robust dual global alignment, and precise matching at three fine-grained levels: node, relation, and focal disproving. Experiments on benchmarks such as DCI and DOCCI demonstrate that POGA performs favorably against several state-of-the-art methods in long-text understanding and complex relation discrimination.
PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding
Authors: Souhail Hadgi; Bingchen Gong; Ramana Sundararaman; Emery Pierson; Lei Li; Peter Wonka; Maks Ovsjanikov
Venue: CVPR 2026 (main)

Abstract: Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent approaches leverage vision and language foundation models to directly solve dense tasks through multi-view renderings and text queries. While promising, these pipelines require expensive inference over multiple renderings, depend heavily on large language-model (LLM) prompt engineering for captions, and fail to exploit the inherent 3D geometry of shapes. We address this gap by introducing an encoder-only 3D model that produces language-aligned patch-level features directly from point clouds. Our pre-training approach builds on existing data engines that generate part-annotated 3D shapes by pairing multi-view SAM regions with VLM captioning. Using this data, we train a point cloud transformer encoder in two stages: (1) distillation of dense 2D features from visual encoders such as DINOv2 into 3D patches, and (2) alignment of these patch embeddings with part-level text embeddings through a multi-positive contrastive objective. Our 3D encoder achieves zero-shot 3D part segmentation with fast single-pass inference without any test-time multi-view rendering, while significantly outperforming previous rendering-based and feed-forward approaches across several 3D part segmentation benchmarks.
Nonlinear Color Transfer via Learnable Bezier Flows
Authors: Junhyoung Lee; Seongwoon Jo; JeongHun Park; Yeonji Ryou; Jeongha Yang; Jangho Kim
Venue: CVPR 2026 (main)

Abstract: Color transfer aims to match the color distribution of a content image (source) to that of a style image (target) while preserving structure and perceptual realism. Yet modulation-based flow models such as ModFlows often produce trajectory misalignment and artifacts because they rely on strictly linear transport paths. We propose NCT, a nonlinear color transfer framework that replaces linear paths with Bezier trajectories, enabling smooth, nonlinear, and perceptually coherent color transfer. This parameterization lets the transport bend toward plausible intermediate color regimes, improving content-style alignment and reducing chromatic distortion. We further incorporate a Mixture of Experts (MoE) module in the encoder to select trajectory experts for different chromatic regimes, improving generalization to heterogeneous data with complex illumination and materials. Experiments show that NCT reduces artifacts and achieves more stable color transfer than prior flow-based methods, especially on 3D-rendered or highly textured images. The code is provided in supplementary materials.
DreamOmni2: Multimodal Instruction-based Generation and Editing
Authors: Bin Xia; bohao peng; Yuechen Zhang; Junjia Huang; Jiyang Liu; Jingyao Li; Haoru Tan; Sitong Wu; Chengyao Wang; Yitong Wang; Bei Yu; Jiaya Jia
Venue: CVPR 2026 (main)

Abstract: Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical user needs. Instruction-based editing relies solely on language instructions, which often fail to capture specific editing details, making reference images necessary. Meanwhile, subject-driven generation is limited to combining concrete objects or people, overlooking broader, abstract concepts. To address these challenges, we propose two novel tasks: multimodal instruction-based editing and generation. These tasks support both text and image instructions and extend the scope to include both concrete and abstract concepts, greatly enhancing their practical applications. We introduce DreamOmni2, tackling two primary challenges: data creation and model framework design. Our data synthesis pipeline consists of three steps: (1) using a feature mixing method to create extraction data for both abstract and concrete concepts, (2) generating multimodal instruction-based editing training data using the editing and extraction models, and (3) further applying the extraction model to create training data for multimodal instruction-based editing. For the framework, to handle multi-image input, we propose an index encoding and position encoding shift scheme, which helps the model distinguish images and avoid pixel confusion. Additionally, we introduce joint training with the VLM and our generation/editing model to better process complex instructions. In addition, we have proposed comprehensive benchmarks for these two new tasks to drive their development. Experiments show that DreamOmni2 achieved impressive results.
TACO: Task-Aware Contrastive Learning for Joint LiDAR Localization and 3D Object Detection
Authors: Leyuan Xing; Huanjia Zhang; Dongyu Pan; Hai Wu; Qiming Xia; Kezheng Xiong; Wen Li; Chenglu Wen; Cheng Wang
Venue: CVPR 2026 (main)

Abstract: Reliable navigation and decision-making of autonomous vehicles require both accurate localization and object detection. Traditionally, these two tasks are handled separately, leading to redundant computation and limited cross-task knowledge transfer. This paper proposes TACO, the first Task-Aware COntrastive learning framework, which performs joint LiDAR localization and 3D object detection within a single, unified network. TACO leverages contrastive learning to explicitly decouple and align static geographic features for localization and object-centric features for detection. This bidirectional mutual supervision not only enhances localization robustness in dynamic environments by filtering dynamic noise but also boosts detection accuracy via effective spatial context. Additionally, we propose OxfoLD, the first dataset that provides multi-traversal LiDAR localization ground truth with rich 3D object annotations, thereby supporting task validation across various times and weather conditions. Experimental results demonstrate that TACO achieves state-of-the-art localization accuracy while maintaining competitive detection performance. The code and dataset will be released.
SAM2Text: Towards Prompt-Free and Multi-Resolution Video Scene Text Segmentation
Authors: Jing-Yao Zhang; Heng Zhang; Mingsen Zhang; Binbin Yang; Fei Yin
Venue: CVPR 2026 (main)

Abstract: We introduce a novel method for video Scene Text Segmentation (STS), a task critical for understanding dynamic visual content. Despite the success of foundation models like Segment Anything Model 2 (SAM2) in generic segmentation, their application to video STS is hindered by the reliance on external prompts, limited output resolution, and instability in video sequences. To address these, we present a comprehensive framework based on SAM2. First, we fine-tune the image encoder using LoRA and integrate a self-prompting module, enabling the model to autonomously generate text-specific prompts. Second, we augment the decoder with additional upsampling branches at 512x512 and 1024x1024 resolutions, complementing the original 256x256 output to produce high-fidelity, multi-resolution masks. Third, we enhance the memory mechanism by combining short-term memory with a top-k selection strategy, ensuring temporally consistent and stable segmentation across video frames. A significant obstacle in video STS is data scarcity. To this end, we contribute two datasets: STS-SynthV, containing 1,410 synthetic video clips generated via FlowText, and STS-RealV, comprising 660 meticulously annotated real-world video sequences. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple video and image scene text benchmarks. The data and code:https://github.com/insuper-zhang/SAM2Text/.
JANUS: A Lightweight Framework for Jailbreaking Text-to-Image Models via Distribution Optimization
Authors: Haolun Zheng; Yu He; Tailun Chen; Shuo Shao; Zhixuan Chu; Hongbin Zhou; Lan Tao; Zhan Qin; Kui Ren
Venue: CVPR 2026 (main)

Abstract: Text-to-image (T2I) models such as Stable Diffusion and DALLE remain susceptible to generating harmful or Not-Safe-For-Work (NSFW) content under jailbreak attacks despite deployed safety filters. Existing jailbreak attacks either rely on proxy-loss optimization instead of the true end-to-end objective, or depend on large-scale and costly RL-trained generators. Motivated by these limitations, we propose JANUS , a lightweight framework that formulates jailbreak as optimizing a structured prompt distribution under a black-box, end-to-end reward from the T2I system and its safety filters. JANUS replaces a high-capacity generator with a low-dimensional mixing policy over two semantically anchored prompt distributions, enabling efficient exploration while preserving the target semantics. On modern T2I models, we outperform state-of-the-art jailbreak methods, improving ASR-8 from 25.30% to 43.15% on Stable Diffusion 3.5 Large Turbo with consistently higher CLIP and NSFW scores. JANUS succeeds across both open-source and commercial models. These findings expose structural weaknesses in current T2I safety pipelines and motivate stronger, distribution-aware defenses. Warning: This paper contains model outputs that may be offensive.
WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing
Authors: Kaihang Pan; Weile Chen; Haiyi Qiu; Qifan Yu; Wendong Bu; Zehan Wang; Yun Zhu; Juncheng Li; Siliang Tang
Venue: CVPR 2026 (main)

Abstract: Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a scope for evaluation, failing to holistically assess these advanced abilities. To address this, we introduce WiseEdit, a knowledge-intensive benchmark for comprehensive evaluation of cognition- and creativity-informed image editing, featuring deep task depth and broad knowledge breadth. Drawing an analogy to human cognitive creation, WiseEdit decomposes image editing into three cascaded steps--Awareness, Interpretation, and Imagination--each corresponding to a task that poses a challenge for models to complete at the specific step. It also encompasses complex tasks, where none of the three steps can be finished easily. Furthermore, WiseEdit incorporates three fundamental types of knowledge: Declarative, Procedural, and Metacognitive knowledge. Ultimately, WiseEdit comprises 1,220 test cases, objectively revealing the limitations of SoTA image editing models in knowledge-based cognitive reasoning and creative composition capabilities.
UniAVGen: Unified Audio and Video Generation with Asymmetric Cross-Modal Interactions
Authors: Guozhen Zhang; Zixiang Zhou; Teng Hu; Ziqiao Peng; Youliang Zhang; Yi Chen; Yuan Zhou; Qinglin Lu; Limin Wang
Venue: CVPR 2026 (main)

Abstract: Due to the lack of effective cross-modal modeling, existing open-source audio-video generation methods often exhibit compromised lip synchronization and insufficient semantic consistency. To mitigate these drawbacks, we propose UniAVGen, a unified framework for human-centric joint audio and video generation. UniAVGen is anchored in a dual-branch joint synthesis architecture, incorporating two parallel Diffusion Transformers (DiTs) to build a cohesive cross-modal latent space. At its heart lies an Asymmetric Cross-Modal Interaction mechanism, which enables bidirectional, temporally aligned cross-attention, thus ensuring precise spatiotemporal synchronization and semantic consistency. Furthermore, this cross-modal interaction is augmented by a Face-Aware Modulation (FAM) module, which dynamically prioritizes salient regions in the interaction process. To enhance generative fidelity during inference, we additionally introduce Modality-Aware Classifier-Free Guidance (MA-CFG), a novel strategy that explicitly amplifies cross-modal correlation signals. Notably, UniAVGen's robust joint synthesis design enables the seamless unification of pivotal audio-visual tasks within a single model. Furthermore, we demonstrate that joint multi-task training can further boost the performance of joint generation. Comprehensive experiments validate that, with far fewer training samples (1.3M vs. 30.1M), UniAVGen delivers overall advantages in audio-video synchronization, timbre consistency, and emotion consistency.
When Anonymity Breaks: Identifying Models Behind Text-to-Image Leaderboards
Authors: Ali Naseh; Anshuman Suri; Yuefeng Peng; Harsh Chaudhari; Alina Oprea; Amir Houmansadr
Venue: CVPR 2026 (main)

Abstract: Text-to-image (T2I) models are increasingly popular, producing a large share of AI-generated images online. To compare model quality, voting-based leaderboards have become the standard, relying on anonymized model outputs for fairness. In this work, we show that such anonymity can be easily broken. We find that generations from each T2I model form distinctive clusters in the image embedding space, enabling accurate deanonymization without prompt control or training data. Using 22 models and 280 prompts (150K images), our centroid-based method achieves high accuracy and reveals systematic model-specific signatures. We further introduce a prompt-level distinguishability metric and conduct large-scale analyses showing how certain prompts can lead to near-perfect distinguishability. Our findings expose fundamental security flaws in T2I leaderboards and motivate stronger anonymization defenses.
PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation
Authors: Baiqin Wang; Xiangyu Zhu; Fan Shen; Hao Xu; Zhen Lei
Venue: CVPR 2026 (main)

Abstract: Recent advancements in audio-driven talking face generation have made great progress in lip synchronization. However, current methods often lack sufficient control over talking face, such as speaking style and emotional expression, resulting in uniform facial motion. In this paper, we focus on improving two key factors: lip-audio alignment control(LAC) and emotion control(EMC), to enhance the diversity and user-friendliness of talking videos. Lip-audio alignment control ensures accurate lip-sync across Recent advancements in audio-driven talking face generation have made great progress in lip synchronization. However, current methods often lack sufficient control over talking face, such as speaking style and emotional expression, resulting in uniform facial motion. In this paper, we focus on improving two key factors: lip-audio alignment control(LAC) and emotion control(EMC), to enhance the diversity and user-friendliness of talking videos. Lip-audio alignment control ensures accurate lip-sync across varied speaking styles to simulate different talking habits, whereas emotion control aims to generate realistic emotional expressions with varying intensities and mixed emotional states. To achieve precise facial animation control, we propose a novel and efficient framework, PC-Talk, which enables lip-audio alignment and emotion control through implicit keypoint deformations. First, our LAC module generates lip-synced talking faces with a specific speaking style, derived from either a video reference or preset options. It also supports lip movement scale adjustment and fine-grained editing of speaking styles for specific articulations. Second, our EMC module produces vivid emotional facial expressions through pure emotional deformation. It further enables precise control over emotion intensity and the compound emotions across different facial regions. Our method demonstrates outstanding control capabilities and achieves SOTA performance on HDTF and MEAD datasets in experiments. Project page: https://bq-wang0511.github.io/PC-Talk/