GenAI4ELab/papercli-papers huggingface dataset:
Event-based Visual Deformation Measurement
Authors: Yuliang Wu; Wei Zhai; Yuxin Cui; Tiesong Zhao; Yang Cao; Zheng-Jun Zha
Venue: CVPR 2026 (main)

Abstract: Visual Deformation Measurement (VDM) aims to recover dense deformation fields by tracking surface motion from camera observations.  Traditional image-based methods rely on minimal inter-frame motion to constrain the correspondence search space, which limits their applicability to highly dynamic scenes or necessitates high-speed cameras at the cost of prohibitive storage and computational overhead. We propose an event-frame fusion framework that exploits events for temporally dense motion cues and frames for spatially dense precise estimation. Revisiting the solid elastic modeling prior, we propose an Affine Invariant Simplicial (AIS) framework. It partitions the deformation field into linearized sub-regions with low-parametric representation, effectively mitigating motion ambiguities arising from sparse and noisy events. To speed up optimization and reduce error accumulation, a neighborhood-greedy optimization strategy is introduced, enabling well-converged sub-regions to guide their poorly-converged neighbors, effectively suppress local error accumulation in long-term dense tracking. To evaluate the proposed method, a benchmark dataset with temporally aligned event streams and frames is established, encompassing over 120 sequences spanning diverse deformation scenarios. Experimental results show that our method outperforms the state-of-the-art baseline by 1.6x in survival rate. Remarkably, it achieves this using only 18.9% of the data storage and processing resources of high-speed video methods.
Masking Matters: Unlocking the Spatial Reasoning Capabilities of LLMs for 3D Scene-Language Understanding
Authors: Yerim Jeon; Miso Lee; WonJun Moon; Jae-Pil Heo
Venue: CVPR 2026 (main)

Abstract: Recent advances in 3D scene-language understanding have leveraged Large Language Models (LLMs) for 3D reasoning by transferring their general reasoning ability to 3D multi-modal contexts. However, existing methods typically adopt standard decoders from language modeling, which rely on a causal attention mask. This design introduces two fundamental conflicts in 3D scene understanding: sequential bias among order-agnostic 3D objects and restricted object-instruction attention, hindering task-specific reasoning. To overcome these limitations, we propose 3D Spatial Language Instruction Mask (3D-SLIM), an effective masking strategy that replaces the causal mask with an adaptive attention mask tailored to the spatial structure of 3D scenes. Our 3D-SLIM introduces two key components: a Geometry-adaptive Mask that constrains attention based on spatial density rather than token order, and an Instruction-aware Mask that enables object tokens to directly access instruction context. This design allows the model to process objects based on their spatial relationships while being guided by the user's task. 3D-SLIM is simple, requires no architectural modifications, and adds no extra parameters, yet it yields substantial performance improvements across diverse 3D scene-language tasks. Extensive experiments across multiple benchmarks and LLM baselines validate its effectiveness and underscore the critical role of decoder design in 3D multi-modal reasoning.
GeoDexGrasp: Geometry-aware Generation for Data-efficient and Physics-plausible Dexterous Grasping
Authors: Bing Han; Weiyuan Liu; Changlong Zhang; Chenxi Wang; Zhibin Zhao; Zhi Zhai
Venue: CVPR 2026 (main)

Abstract: Achieving dexterous grasping remains a key challenge in robotics. Recent generative approaches enable diverse grasps through large-scale data-driven training, yet they often neglect geometric priors of objects, which leads to low data efficiency and poor physical plausibility. We propose GeoDexGrasp, a geometry-aware generation framework for dexterous grasping built upon object-centric geometric representations. We introduce a SIM(3)-equivariant network equipped with a self-supervised disentanglement strategy to extract interpretable and transferable geometric features, including shape, size, pose, and interaction direction. The overall generation process is then decomposed into two stages: first, root rotation generation conditioned on pose and interaction direction; second, hand grasp generation guided by shape and size. By leveraging geometric representations, GeoDexGrasp achieves SOTA physical plausibility (reducing 40% penetration depth) across five datasets, and exhibits improved data efficiency. Additionally, GeoDexGrasp is also lightweight (using less than 20% of the parameters of the previous SOTA method) and attains a comparable grasp success rate.
PriVi: Towards a General-Purpose Video Model for Primate Behavior in the Wild
Authors: Felix B. Mueller; Jan F. Meier; Timo Lueddecke; Richard Vogg; Roger L. Freixanet; Valentin Hassler; Tiffany Bosshard; Elif Karakoc; William J. O'Hearn; Sofia M. Pereira; Sandro Sehner; Kaja Wierucka; Judith Burkart; Claudia Fichtel; Julia Fischer; Alexander Gail; Catherine Hobaiter; Julia Ostner; Liran Samuni; Oliver Schülke; Neda Shahidi; Erin G. Wessling; Alexander S. Ecker
Venue: CVPR 2026 (main)

Abstract: Non-human primates are our closest living relatives, and analyzing their behavior is central to research in cognition, evolution, and conservation. Computer vision could greatly aid this research, but existing methods often rely on human-centric pretrained models and focus on single datasets, which limits generalization. We address this limitation by shifting from a model-centric to a data-centric approach and introduce PriVi, a large-scale primate-centric video pretraining dataset. PriVi contains 424 hours of curated video, combining 174 hours from behavioral research across 11 settings with 250 hours of diverse web-sourced footage, assembled through a scalable data curation pipeline. We continue pretraining V-JEPA, a large-scale video model, on PriVi to learn primate-specific representations and evaluate it using a lightweight frozen classifier. Across four benchmark datasets - ChimpACT, PanAf500, BaboonLand, and ChimpBehave - our approach consistently outperforms prior work, including fully finetuned baselines, and scales favorably with fewer labels. These results demonstrate for the first time that domain-level pretraining, where pretraining is conducted on similar data but not the target dataset itself, works for video models. Our primate-centric pretraining substantially improves data efficiency and generalization, making it a promising approach for low-label applications. Dataset, code, and models are available at https://privi.eckerlab.org.
Beyond Soft Label: Dataset Distillation via Orthogonal Gradient Matching
Authors: Deyu Bo; Xinchao Wang
Venue: CVPR 2026 (main)

Abstract: Condensing the large-scale, high-resolution ImageNet-1K dataset remains a challenge for dataset distillation (DD). Existing methods typically match batch normalization (BN) statistics, i.e., mean and variance, between real and synthetic datasets. Although effective with soft labels, their performance degrades substantially under hard labels. In this paper, we theoretically identify that BN matching mainly aligns the scales of real and synthetic gradients but overlooks their directions. However, experimental evidence demonstrates that gradient direction, rather than scale, is pivotal to model training, clarifying the limitations of prior methods. Building on this insight, we introduce Orthogonal Gradient Matching (OGM), which explicitly aligns the intrinsic direction of gradients, i.e., singular vectors. Specifically, OGM first orthogonalizes real and synthetic gradients by setting all singular values to one, eliminating their scales, and then minimizes the distance between these orthogonal gradients so that their singular vectors coincide. To further reduce computation, OGM employs a least-squares loss whose gradients can be obtained in the forward pass, avoiding back-propagation. Extensive experiments on ImageNet-1K validate the effectiveness of OGM. With only ten images per class (IPC = 10), OGM achieves 47.0% accuracy with soft labels and 16.7% with hard labels, outperforming training-based DD methods and RDED.
CUBic: Coordinated Unified Bimanual Perception and Control Framework
Authors: Xingyu Wang; Pengxiang Ding; Jingkai Xu; Donglin Wang; Zhaoxin Fan
Venue: CVPR 2026 (main)

Abstract: Recent advances in visuomotor policy learning have enabled robots to perform control directly from visual inputs. Yet, extending such end-to-end learning from single-arm to bimanual manipulation remains challenging due to the need for both independent perception and coordinated interaction between arms. Existing methods typically favor one side--either decoupling the two arms to avoid interference or enforcing strong cross-arm coupling for coordination--thus lacking a unified treatment. We propose CUBic, a Coordinated and Unified framework for Bimanual perception and control  that reformulates bimanual coordination as a unified perceptual modeling problem. CUBic learns a shared tokenized representation bridging perception and control, where independence and coordination emerge intrinsically from structure rather than from hand-crafted coupling. Our approach integrates three components: unidirectional perception aggregation, bidirectional perception coordination through two codebooks with shared mapping, and a unified perception-to-control diffusion policy. Extensive experiments on the RoboTwin benchmark show that CUBic consistently surpasses standard baselines, achieving marked improvements in coordination accuracy and task success rates over state-of-the-art visuomotor baselines.
CHIPS: Efficient CLIP Adaptation via Curvature-aware Hybrid Influence-based Data Selection
Authors: Xinlin Zhuang; Yichen Li; Xiwei Liu; Haolin Yang; Yifan Lu; Ziyun Zou; Yulong Li; Huifa Li; Dongliang Chen; Qinglei Wang; Weiyang Liu; Ying Qian; Jiangming Shi; Imran Razzak
Venue: CVPR 2026 (main)

Abstract: Adapting CLIP to vertical domains is typically approached by novel fine-tuning strategies or by continual pre-training (CPT) on large domain-specific datasets. Yet, data itself remains an underexplored factor in this process. We revisit this task from a data-centric perspective: Can effective data selection substitute for large-scale datasets in CPT? We introduce CHIPS (Curvature-aware Hybrid Influence in Projection Subspace), which assigns each image-text pair a utility score that integrates three complementary factors aligned with three goals: faithfulness via a curvature-aware and Newton-style alignment computed in CLIP's end-point subspace; scalability via an InfoNCE-aware curvature estimator with Johnson-Lindenstrauss (JL) sketching; and retention via a selection-aware relevance weight combined with learnability to balance target adaptation against general-domain preservation. We justify this design theoretically by proving a lower-bound guarantee on the proxy's correlation with full-parameter alignment and by characterizing the bias-variance trade-offs introduced by curvature mixing and JL sketching. We evaluate CHIPS empirically across various settings: 1) CHIPS attains state-of-the-art performance among selection baselines on 17 medical benchmarks, matches full-dataset CPT with 30% of the data, and outperforms half-dataset CPT using only 10%; 2) on 31 general-domain benchmarks, CHIPS yields the least performance drop under all retention ratios.
Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Authors: Chi-Pin Huang; Yunze Man; Zhiding Yu; Min-Hung Chen; Jan Kautz; Yu-Chiang Frank Wang; Fu-En Yang
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy reasoning traces. We propose Fast-ThinkAct, an efficient reasoning framework that achieves compact yet performant planning through verbalizable latent reasoning. Fast-ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference-guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodied control. This enables reasoning-enhanced policy learning that effectively connects compact reasoning to action execution. Extensive experiments across diverse embodied manipulation and reasoning benchmarks demonstrate that Fast-ThinkAct achieves strong performance with up to 89.3% reduced inference latency over state-of-the-art reasoning VLAs, while maintaining effective long-horizon planning, few-shot adaptation, and failure recovery.
Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images
Authors: Hongyuan Liu; Bochao Zou; Qiankun Liu; Haochen Yu; Qi Mei; Jianfei Jiang; Chen Liu; Cheng Bi; Zhao Wang; Xueyang Zhang; Yifei Zhan; Jiansheng Chen; Huimin Ma
Venue: CVPR 2026 (main)

Abstract: Creating realistic and simulation-ready 3D assets is crucial for autonomous driving research and virtual environment construction. However, existing 3D vehicle generation methods are often trained on synthetic data with significant domain gaps from real-world distributions. The generated models often exhibit arbitrary poses and undefined scales, resulting in poor visual consistency when integrated into driving scenes. In this paper, we present Unposed-to-3D, a novel framework that learns to reconstruct 3D vehicles from real-world driving images using image-only supervision. Our approach consists of two stages. In the first stage, we train an image-to-3D reconstruction network using posed images with known camera parameters. In the second stage, we remove camera supervision and use a camera prediction head that directly estimates the camera parameters from unposed images. The predicted pose is then used for differentiable rendering to provide self-supervised photometric feedback, enabling the model to learn 3D geometry purely from unposed images. To ensure simulation readiness, we further introduce a scale-aware module to predict real-world size information, and a harmonization module that adapts the generated vehicles to the target driving scene with consistent lighting and appearance. Extensive experiments demonstrate that Unposed-to-3D effectively reconstructs realistic, pose-consistent, and harmonized 3D vehicle models from real-world images, providing a scalable path toward creating high-quality assets for driving scene simulation and digital twin environments.
Geometry-Guided 3D Visual Token Pruning for Video-Language Models
Authors: Han Li; Zehao Huang; Jiahui Fu; Naiyan Wang; Si Liu
Venue: CVPR 2026 (main)

Abstract: Multimodal large language models have demonstrated remarkable capabilities in 2D vision, motivating their extension to 3D scene understanding. Recent studies represent 3D scenes as 3D spatial videos composed of image sequences with depth and camera pose information, enabling pre-trained video-language models to perform 3D reasoning tasks. However, the large number of visual tokens in spatial videos remains a major bottleneck for efficient inference. Existing pruning methods overlook the view consistency of spatial videos and the spatial diversity of the remaining tokens, which prevents them from effectively removing inter-frame redundancy and preserving scene completeness. In this paper, we propose Geo3DPruner, a Geometry-Guided 3D Visual Token Pruning framework. Geo3DPruner first models cross-frame relevance through geometry-aware global attention, and then performs a two-stage pruning process. The intra-voxel stage selects representative multi-view features within each voxel, while the inter-voxel stage preserves spatial diversity by selecting a globally distributed subset of voxels. Extensive experiments on multiple 3D scene understanding benchmarks demonstrate that Geo3DPruner retains over 90% of the original performance while pruning 90% of visual tokens, significantly outperforming existing text-guided and vision-guided pruning methods.
DiP: Taming Diffusion Models in Pixel Space
Authors: Zhennan Chen; Junwei Zhu; Xu Chen; Jiangning Zhang; Xiaobin Hu; Hanzhen Zhao; Chengjie Wang; Jian Yang; Ying Tai
Venue: CVPR 2026 (main)

Abstract: Diffusion models face a fundamental trade-off between generation quality and computational efficiency. Latent Diffusion Models (LDMs) offer an efficient solution but suffer from potential information loss and non-end-to-end training. In contrast, existing pixel space models bypass VAEs but are computationally prohibitive for high-resolution synthesis. To resolve this dilemma, we propose DiP, an efficient pixel space diffusion framework. DiP decouples generation into a global and a local stage: a Diffusion Transformer (DiT) backbone operates on large patches for efficient global structure construction, while a co-trained lightweight Patch Detailer Head leverages contextual features to restore fine-grained local details. This synergistic design achieves computational efficiency comparable to LDMs without relying on a VAE. DiP is accomplished with up to 10xfaster inference speeds than previous method while increasing the total number of parameters by only 0.3%, and achieves an 1.79 FID score on ImageNet 256x256.
ReasonX: MLLM-Guided Intrinsic Image Decomposition
Authors: Alara Dirik; Tuanfeng Yang Wang; Duygu Ceylan; Stefanos Zafeiriou; Anna Frühstück
Venue: CVPR 2026 (main)

Abstract: Intrinsic image decomposition aims to separate images into physical components such as albedo, depth, normals, and illumination. While recent diffusion- and transformer-based models benefit from paired supervision from synthetic datasets, their generalization to diverse, real-world scenarios remains challenging. We propose ReasonX, a novel framework that leverages a multimodal large language model (MLLM) as a perceptual judge providing relative intrinsic comparisons, and uses these comparisons as GRPO rewards for fine-tuning intrinsic decomposition models on unlabeled, in-the-wild images. Unlike RL methods for generative models, our framework aligns conditional intrinsic predictors by rewarding agreement between the judge's relational assessments and analytically derived relations from the model's outputs. ReasonX is model-agnostic and can be applied to different intrinsic predictors. Across multiple base architectures and modalities, ReasonX yields significant improvements, including 9-25% WHDR reduction on IIW albedo and up to 46% depth accuracy gains on ETH3D, highlighting the promise of MLLM-guided comparative supervision to bridge low- and high-level vision reasoning.
DyFCLT: Dynamic Frequency-Decoupled Cross-Modal Learning Transformer for Multimodal Tiny Object Detection
Authors: Chaolang Li; Pengwen Dai; Jingyu Li; Siyuan Yao; Yuchen Jiang; Zhuoran Zheng
Venue: CVPR 2026 (main)

Abstract: Multimodal tiny object detection plays a critical role in real-world applications, yet remains highly challenging due to weak target representations and complex cross-modal interference. Existing frequency-domain methods for tiny object detection are still largely limited to the visible modality and overlook complementary cross-modal frequency cues in multimodal scenes. In this paper, we investigate cross-modal frequency learning for RGBT tiny object detection. Through frequency characteristic analysis, we find that tiny objects in both RGB and infrared modalities contain richer mid- and high-frequency components as object size decreases. Motivated by this observation, we propose a Dynamic Frequency-Decoupled Cross-Modal Learning Transformer (DyFCLT). Specifically, DyFCLT introduces a Dynamic Frequency-Band Decoupled Cross-Modal Attention (DFCA) mechanism to perform fine-grained cross-modal interaction across dynamic frequency sub-bands, and a Selective Smoothing Enhancement (SSE) module to suppress background noise and enhance foreground responses during multi-scale fusion. Extensive experiments on two RGBT tiny object detection benchmarks and one general-scale benchmark demonstrate that DyFCLT achieves state-of-the-art performance with strong generalization across different scales and scenes.
ReLaX: Reasoning with Latent Exploration for Large Reasoning Models
Authors: Shimin Zhang; Xianwei Chen; Yufan Shen; Ziyuan Ye; Jibin Wu
Venue: CVPR 2026 (main)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated remarkable potential in enhancing the reasoning capability of Large Reasoning Models (LRMs). However, RLVR often drives the policy toward over-determinism, resulting in ineffective exploration and premature policy convergence. While promoting token-level diversity has shown promise in mitigating entropy collapse, we argue that the latent dynamics underlying token generation encode a far richer computational structure for steering policy optimization toward a more effective exploration-exploitation tradeoff. To enable tractable analysis and intervention of the latent dynamics of LRMs,  we leverage Koopman operator theory to obtain a linearized representation of their hidden state dynamics. This enables us to introduce Dynamic Spectral Dispersion (DSD), a new metric to quantify the heterogeneity of the model's latent dynamics, serving as a direct indicator of policy exploration. Building upon these foundations, we propose Reasoning with Latent eXploration (ReLaX), a framework that explicitly incorporates latent dynamics to regulate exploration and exploitation during policy optimization. Comprehensive experiments across a wide range of multimodal and text-only reasoning benchmarks show that ReLaX consistently incentivizes reasoning capability and outperforms existing token-level methods. Our project is available at https://github.com/ZhangShimin1/ReLaX.
SketchAssist: A Practical Assistant for Semantic Edits and Precise Local Redrawing
Authors: Han Zou; Yan Zhang; Ruiqi Yu; Cong Xie; Jie Huang; Zhenpeng Zhan
Venue: CVPR 2026 (main)

Abstract: Sketch editing requires jointly handling high-level semantic changes and precise local redrawing, a combination that is particularly challenging for sparse, style-sensitive line art. Unlike natural images, sketches rely on minimal visual cues, making it difficult for existing methods to reconcile global semantic modifications with fine-grained structural control while preserving overall coherence.  We present SketchAssist, an interactive sketch assistant that unifies instruction-guided editing with line-guided region redrawing, enabling efficient and controllable sketch manipulation while preserving overall composition. To support this task, we introduce a controllable data generation pipeline that constructs structured edit sequences with precise attribute variations and maintains structural alignment across multi-step modifications, while expanding stylistic diversity via style-preserving transformations. Building on this data, SketchAssist adopts a unified framework based on DiT, using a multi-channel input representation to encode sketches, masks, and guidance signals within a single interface. To further handle different editing modes, we integrate a Task-guided Mixture-of-Experts (T-MoE) into LoRA layers, enabling adaptive control over semantic and structural guidance. Extensive experiments demonstrate state-of-the-art performance on both tasks, achieving strong instruction adherence and improved structural and style consistency compared to recent methods. Together, our method provide a practical and controllable solution for sketch editing.