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ECCV 2026

TextFace: Compositional Text-Guided Identity Preserving Face Synthesis for Face Recognition

Junzhe Yang, Mingjie He, Shiguang Shan

Identity preserving face synthesis (IPFS) aims to generate large-scale virtual face datasets to train robust face recognition (FR) models while mitigating privacy and bias issues of web-crawled data. While diffusion-based IPFS methods have made huge progress, FR models trained on synthetic data still underperform those trained on real images. This gap stems from limited style diversity and insufficient intraidentity variation. Through analysis, we find that identity embeddings from real images primarily encode core facial features and inherently contain intra-identity variation. To generate more realistic virtual images, we propose TextFace, a diffusion-based IPFS framework that combines compositional text control for overall style diversity and a principled score-space identity blending strategy to enhance intra-identity variation. The identity-irrelevant condition factors are modeled by compositional language attributes, while identity-inherent factors are diversified by identity blending, which injects small, stable structural variations during denoising without sacrificing identity consistency. Extensive experiments show that FR models trained on TextFace synthetic data consistently outperform prior IPFS pipelines and substantially narrow the gap to real-data training.

ECCV 2026

AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion

Xirui Liang, Jingkai Xu, Jiaqi Liang, Yuran Wang, Ruochong Li, Yuanpei Chen, Masayoshi Tomizuka, Wei Zhan, Ruihai Wu

Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback, a capability that remains challenging to replicate in robotic systems. Existing robotic grasping approaches predominantly rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact, limiting robustness and generalization. To address this challenge, we propose a uni(cid:28)ed visuo-tactilefusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive re(cid:28)nement. At its core, our method introduces an e(cid:30)cient visuo-tactile representation that tightly fuses object geometry with tactile feedback by associating tactile signals with (cid:28)nger identities. This uni(cid:28)ed representation supports contact-aware grasp pose generation during planning and tactile-guided re(cid:28)nement after contact, enabling the system to reason about (cid:28)ne-grained (cid:28)nger-object interactions and adjust grasps dynamically. Comprehensive experiments in both simulation and real-world environments demonstrate that our approach signi(cid:28)cantly enhances grasp success rates and generalization across diverse objects. • •

ECCV 2026

GRAFT: Geometric Refinement and Fitting Transformer for Human Scene Reconstruction

Pradyumna YM, Yuxuan Xue, Yue Chen, Nikita Kister, István Sárándi, Gerard Pons-Moll

Reconstructing physically plausible 3D human-scene interactions (HSI) from a single image currently presents a trade-off: optimization based methods offer accurate contact but are slow (∼20s), while feed-forward approaches are fast yet lack explicit interaction reasoning, producing floating and interpenetration artifacts. Our key insight is that geometry-based human–scene fitting can be amortized into fast feed-forward inference. We present GRAFT (Geometric Refinement And Fitting Transformer), a learned HSI prior that predicts Interaction Gradients: corrective parameter updates that iteratively refine human meshes by reasoning about their 3D relationship to the surrounding scene. GRAFT encodes the interaction state into compact body-anchored tokens, each grounded in the scene geometry via Geometric Probes that capture spatial relationships with nearby surfaces. A lightweight transformer recurrently updates human meshes and re-probes the scene, ensuring the final pose aligns with both learned priors and observed geometry. GRAFT operates either as an end-to-end reconstructor using image features, or with geometry alone as a transferable plug-and-play HSI prior that improves feed-forward methods without retraining. Experiments show GRAFT improves interaction quality by up to 122% over state-of-the-art feed-forward methods and matches optimization-based interaction quality at ∼100× lower runtime, while generalizing seamlessly to in-the-wild multi-person scenes and being preferred in 64.8% of threeway user study. Project page: https://pradyumnaym.github.io/graft.

ECCV 2026

MCVL: Multi-Space Cross-View Learning for Aerial-Ground Person Re-Identification

Wajahat Khalid, Bin Liu, Xulin Li, Yubo Wang, Muhammad Sher Afgan

The core challenge of Aerial-Ground Person Re-Identification (AG-ReID) is the drastic viewpoint discrepancy, which induces a significant cross-view feature gap that hinders aerial-ground matching. However, existing AG-ReID methods overlook this gap and attempt to learn direct global semantic correspondences through auxiliary components (e.g., attributes, tokens, or prompts), which limits training effectiveness and increases computational overhead. To address these issues, we propose a novel AG-ReID framework, MCVL, which effectively bridges the cross-view gap through multi-space cross-view learning. Our method aligns both spatial and global feature spaces, learning view-invariant yet identity-discriminative representations to effectively handle viewpoint discrepancies. Specifically, we introduce a Deformable Homography Transformation (DHT) module that projects features across aerial and ground view planes, creating an intermediate cross-view spatial feature space. Based on this, we propose a Cross-View Learning (CVL) strategy that leverages both original and projected features to reduce the spatial feature gap and enhance identity discrimination while preserving perspective invariance. Additionally, we design a View Decorrelation Contrastive Loss (VDCL) to mitigate view-specific biases by pushing global features away from their corresponding view-specific prototypes while pulling together global features of the same identity, thereby suppressing view-related cues and strengthening identity-related representations. Extensive experiments on five AG-ReID datasets, including CARGO, AG-ReIDv1, AG-ReIDv2, LAGPeR, and G2APS-ReID, demonstrate the effectiveness of our proposed MCVL method.

ECCV 2026

Learning Active Perception for Pixel-Space Reasoning via Visual-Intent Stratified GRPO

Mingkang Zhu, Xi Chen, Senqiao Yang, Bei Yu, Hengshuang Zhao, Jiaya Jia

Pixel-space reasoning enables Vision-Language Models to perform active perception: instead of answering from a single global view, the model can zoom into high-resolution regions to gather finegrained evidence. This mirrors human visual problem solving, which involves broad exploration (finding relevant cues) and local verification (confirming details). However, most pixel-space reasoning systems are trained with Group Relative Policy Optimization (GRPO), whose groupwise advantage normalization directly compares sampled rollouts that follow different perception strategies. Rollouts with different zoom depths and different visual intents (explore vs. verify) are normalized together, making exploratory multi-zoom behavior appear disadvantageous: the multi-zoom strategies newly discovered during training typically yield lower mean rewards than already-mastered single-zoom or no-zoom strategies. This leads to visual laziness: the policy does not explore active multi-zoom perception strategies and collapses into a zoom-averse state. To address this, we propose Visual-Intent Stratified GRPO (VIS-GRPO), a drop-in replacement for GRPO that restores fair learning signals for active perception. VIS-GRPO computes advantages only among strategically comparable rollouts by stratifying trajectories along (i) zoom depth and (ii) visual intent, separating broad search over diverse regions from local verification over overlapping regions. This alignment prevents exploratory trajectories from being overshadowed and encourages the learning of active perception. Extensive experiments demonstrate that VIS-GRPO enables effective multi-zoom active perception strategies and consistently improves performance across challenging visual understanding benchmarks like HR-Bench and MME-RealWorld.

ECCV 2026

HorizonRelight: Relighting Long-horizon Videos Consistently via Diffusion Transformers

Jing Yang, Mayoore Jaiswal, ZiAn Wang, Xiao Zeng, Yajie Zhao, Jianyuan Min, Rochelle Pereira

Diffusion-based video relighting enables controllable relighting from a single input video, but modern video diffusion backbones are trained on short clips and applied to long-horizon videos through chunked sliding-window inference, often causing temporal discontinuities at chunk boundaries. We address this by reframing long-horizon relighting as temporally conditioned latent domain 38 translation. Our framework enforces cross-chunk continuity by propagating targetdomain latents across boundaries and makes this behavior learnable using masked target-domain self-conditioning, training the model to continue from temporally masked propagated context. We further introduce warm-start prompting with a relit prompt anchor from a controllable generative model, which establishes the initial target-domain state and creates a general interface for prompt-based relighting. Experiments on in-the-wild long-horizon videos show markedly improved temporal consistency, with chunk-boundary artifacts largely reduced and unwantedappearancechangesacrosschunksgreatlysuppressed.Visitourwebpage at https://research.nvidia.com/labs/sil/projects/horizonrelight/.

ECCV 2026

Penetration-Free Compositional 3D Generation via Gaussian Surface Offset

Yilin Shao, Licheng Jiao, Lingling Li, Xu Liu, Fang Liu, wenping ma, Long Sun, Jiaxuan Zhao

Compositional text-to-3D generation aims to create multiobject scenes from text descriptions, yet existing approaches often suffer from severe inter-object penetrations during joint optimization, where entangled object representations lead to physically implausible scenes. To address this, we propose CompSAG to enable penetration-free and complete compositional 3D generation. Our key insight is a Gaussian surface offset (GSO) mechanism that mimics real-world repulsive forces to effectively resolve inter-object penetrations. The proposed GSO calculates offset values by aggregating repulsive forces from an object proxy surface, pushing apart the Gaussians in collision regions. Furthermore, during appearance optimization, we design a Gaussian surface rectification module to suppress the spiky geometric artifacts introduced by SDS. These are built upon a layout-guided scene initialization driven by a fine-tuned LLM, which provides precise spatial grounding from the text. Extensive experiments across diverse multi-object scenarios demonstrate that CompSAG produces complete, spatially coherent, and penetrationfree 3D scenes, outperforming prior compositional generation baselines.

ECCV 2026

SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers

Jianing Deng, Yuanzhe Li, Jialu Wang, Song Wang, Tianlong Chen, Huanrui Yang, Jingtong Hu

Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success. However, scaling them to long image sequences remains challenging, as the quadratic complexity of cross-view global attention quickly becomes the dominant computational bottleneck. While recent efforts attempt to improve efficiency through compressed or sparse attention, they fail to fully exploit the inherent sparsity and dynamic behavior of global attention. In this work, we present a comprehensive analysis of global attention across multiple F3R transformers and reveal that attention patterns are highly heterogeneous, dynamic, and extremely sparse across layers and attention heads. Motivated by these findings, we propose SAF3R, a training-free dynamic sparse attention framework tailored to F3R transformers. SAF3R integrates tailored sparse attention mechanisms with offline head profiling and an efficient online adaptation strategy to match input-dependent attention behaviors. Extensive experiments demonstrate that SAF3R achieves high sparsity ratios while preserving camera pose estimation and 3D reconstruction quality, translating into substantial end-to-end speedup on F3R transformers compared to existing methods. Code is available at https://github.com/jndeng/SAF3R.

ECCV 2026

Multiple Images Distract Large Multimodal Models via Attention Fragmentation

Tingrui Qiao, Di Zhao, Yuzhuo Li, Bo Pang, Caroline Walker, Chris Cunningham, Yun Sing Koh

While many tasks require reasoning across multiple images, open-source Large Multimodal Models (LMMs) remain unreliable in these settings. We analyze multi-image LMMs and identify a phenomenon we term attention fragmentation: in each image, tokens at similar background locations act as attention sinks, absorbing disproportionate attention. Causal masking further skews this effect, as earlier images accumulate more sink attention than later images. Using an entropy score over per-image attention, we find that visual focus remains highly dispersed across images rather than isolating key evidence. By applying Pinsker’s inequality, we establish a theoretical bound showing that this high-entropy dispersion, combined with stronger early-image sinks, strictly reduces the usable non-sink attention available to earlier images, providing a mechanistic link to image order sensitivity. Motivated by this diagnosis, we propose Attention Remasking (AR), a post-training edit that blocks sink keys and opens a sparse set of cross-image links, routing the recovered attention to task-relevant tokens. AR improves accuracy and reduces order sensitivity across multi-image benchmarks, narrowing the gap between open-source LMMs and leading commercial models.

ECCV 2026

TimeWalker: Personalized Neural Space for Lifelong Head Avatars

Dongwei Pan, Yang Li, Hongsheng Li, Kwan-Yee Lin

We present TimeWalker, a novel framework that models realistic, full-scale 3D head avatars of a person on lifelong scale. Unlike current human head avatar pipelines that capture a person’s identity only at the momentary level (i.e., instant photography, or short videos), TimeWalker constructs a person’s comprehensive identity from unstructured data collection over his/her various life stages, offering a paradigm to achieve full reconstruction and animation of that person at different moments of life. At the heart of TimeWalker’s success is a novel neural parametric model that learns personalized representation with the disentanglement of shape, expression, and appearance across ages. Central to our methodology are the concepts of two aspects: 1) We track back to the principle of modeling a person’s identity in an additive combination of his/her average head representation in the canonical space, and moment-specific head attribute representations driven from a set of neural head basis. To learn the set of head basis that could represent the comprehensive head variations of the target person in a compact manner, we propose a Dynamic Neural Basis-Blending Module (Dynamo). It dynamically adjusts the number and blend weights of neural head bases, according to both shared and specific traits of the target person over ages. 2) We introduce Dynamic 2D Gaussian Splatting (DNA-2DGS), an extension of Gaussian splatting representation, to model head motion deformations like facial expressions without losing the realism of rendering and reconstruction of the full head. DNA-2DGS includes a set of controllable 2D oriented planar Gaussian disks that utilize the priors from a parametric morphable face model, and move/rotate with the change of expression. Through extensive experimental evaluations, we show TimeWalker’s ability to reconstruct and animate avatars across decoupled dimensions with realistic rendering effects, demonstrating a way to achieve personalized “time traveling” in a breeze. Project page: https://TimeWalker2026.github.io/.

ECCV 2026

Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics

Enshen Zhou, Yibo Li, Jingkun An, Jiayuan Zhang, Shanyu Rong, Mengzhen Liu, Yi Han, Yuheng Ji, Huajie Tan, Jiawei He, Pengwei Wang, Zhongyuan Wang, Cheng Chi, Lu Sheng, Shanghang Zhang

Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metricgrounded reasoning compounded with complex spatial referring and realworld metric measurement. However, existing methods struggle with this compositional task. To this end, we propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring via a universal spatial encoder and a regression-supervised decoder to enhance scale awareness during supervised fine-tuning (SFT). Moreover, RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning (RFT) with metric-sensitive process rewards, supervising key intermediate perceptual cues to accurately generate spatial traces. To support SFT and RFT training, we introduce TraceSpatial, a largescale dataset of 30M QA pairs, spanning outdoor/indoor/tabletop scenes and supporting complex reasoning processes (up to 9 steps). We further present TraceSpatial-Bench, a challenging benchmark filling the gap to evaluate spatial tracing. Experimental results show that RoboTracer surpasses baselines in spatial understanding, measuring, and referring, with an average success rate of 79.1%, and also achieves SOTA performance on TraceSpatial-Bench by a large margin, exceeding Gemini- 2.5-Pro by 36% accuracy. Notably, RoboTracer can be integrated with various control policies to execute long-horizon, dynamic tasks across diverse robots (UR5, G1 humanoid) in cluttered real-world scenes. Please see the project page at https://zhoues.github.io/RoboTracer.

ECCV 2026

Rethinking Reward Signals in Video GRPO: When Scores Become Targets

Rui Li, Yuanzhi Liang, Ziqi Ni, Haibin Huang, Chi Zhang, Xuelong Li

Group Relative Policy Optimization (GRPO) enables stable and preference-oriented updates via group-wise comparisons for posttraining video generation. However, GRPO directly optimizes rewardinduced advantages. Under sustained optimization, the reward score can lose fidelity as a proxy for true video quality, consistent with the phenomenon described by Goodhart’s Law. This leads to two recurring issues: (i) shortcut-driven optimization under composite objectives and (ii) reward saturation within prompt groups. To address these issues, we introduce TaRoS, a Target-Robust Reward Signaling framework for Video generation GRPO. TaRoS leverages component level performance assessment together with intra-group sparsity to organize multi-aspect rewards towards optimization objectives. In addition, it adaptively downweights components that exhibit saturation, thereby preserving effective optimization directions and mitigating redundancy. This maintains meaningful optimization directions and preserves within-group ranking separation, thereby preventing reward hacking and leading to more reliable policy updates. Extensive experiments show consistent improvements in visual fidelity, motion coherence, and text-video alignment over strong baselines.

ECCV 2026

PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation

Yuhang Wu, Shuxiang Zhang, WEE CHING, Chi Zhang, Miao Liu

Recent text-to-image models such as DALL·E-3 excel at following diverse prompts yet remain blind to individual aesthetic preferences. We study personalized image generation, where models must align outputs with a user’s implicit visual preferences based on a few historically preferred images and a short prompt. To this end, we introduce PIPBench, the first profile-inclusive benchmark for evaluating personalized image generation. We further propose a novel data construction pipeline that leverages psychological and demographic profiling dimensions for both real-user data collection and scalable agentbased data generation. Using PIPBench, we conduct a thorough evaluation of representative line of methods. Our experiments reveal key limitations in existing methods, suggesting new challenges and opportunities for personalized text-to-image synthesis. Project page: https: //wuyuhang05.github.io/PIPBench/

ECCV 2026

High-Throughput Event-Based Feature Detection and Tracking on an Embedded CPU

Ethan Elms, Yasir Latif, Tat-Jun Chin

Event cameras provide asynchronous visual sensing, making them a compelling modality for agile edge AI systems. For practical deployment, event-based vision algorithms must sustain sufficiently high throughput to keep pace with the incoming event stream. Yet existing approaches often fail to do so, as they are rarely designed in close consideration of the underlying sensor and compute hardware. We propose a new event-based feature detection and tracking algorithm that operates at high throughput on an embedded CPU, without GPU acceleration. Our pipeline is motion-defined: it activates only when sufficient motion is detected and dynamically adapts its computational load to the observed motion. At its core is a novel slice-parallel formulation of event-based corner detection and tracking that operates directly on live event streams. The pipeline is enabled by our Motion-Defined Concurrent Slices architecture, which efficiently processes event data and maximizes utilization of available compute resources. On event streams generated by handheld motion in typical scenes, our method achieves throughput above the average event rate, supporting responsive event-based vision on the edge. Extensive benchmarking on standard datasets and live demonstrations confirms the effectiveness of the proposed method. 1

ECCV 2026

Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field

Licheng Zhong, Gim Hee Lee

Non-prehensile manipulation is often used as a preparatory step for robotic grasping, yet existing approaches typically require a predefined target object pose. In practice, however, objects admit multiple graspable configurations and the desired pose is not known in advance. We reformulate non-prehensile manipulation for grasping as optimizing an object centric graspability objective rather than reaching a specific pose. We construct a graspable set from synthesized grasps and define a graspability field that measures how suitable an object configuration is for successful grasp execution. The scalar measure provides a dense learning signal for reinforcement learning and determines when to terminate manipulation. This yields a closed-loop manipulation-to-grasp pipeline driven by a single policy. Experiments in simulation and on a real robot show that the policy reliably reconfigures objects into graspable states and transitions to grasping without external planners or manually specified stopping conditions. The predicted graspability distance correlates with real world grasp success, which indicates that the learned representation captures grasp feasibility of object configurations.

ECCV 2026

OmniFit: Multi-modal 3D Body Fitting via Scale-agnostic Dense Landmark Prediction

Zeyu Cai, Yuliang Xiu, Renke Wang, Zhijing Shao, Xiaoben Li, YU Siyuan, Chao Xu, Yang Liu, Baigui Sun, Jian Yang, Zhenyu Zhang

Fitting an underlying body model to 3D clothed human assets has been extensively studied, yet most approaches focus on either single-modal inputs such as point clouds or multi-view images alone, often requiring known metric scale—a constraint that is frequently unavailable, particularly for AIGC-generated assets where scale distortion is prevalent. We propose OmniFit, where “Omni” signifies our method’s ability to seamlessly handle diverse multi-modal inputs (e.g., full scans, partial depth, image captures) while remaining scale-agnostic across both real and synthetic assets. Our key innovation is a simple yet effective conditional transformer decoder that directly transforms surface points into dense body landmarks, which are subsequently used for SMPL-X parameter fitting. Additionally, an optional plug-and-play image adapter enriches geometric details with visual cues to address potential incompleteness. We further introduce a dedicated scale predictor to resize subjects into canonical proportions. Remarkably, OmniFit substantially outperforms state-of-the-art methods by 57.1%–80.9% across daily and loose clothing scenarios, making it the first body fitting method to surpass multi-view optimization baselines and the first to achieve millimeter-level accuracy on CAPE and 4D-DRESS benchmarks.

ECCV 2026

VisTa3D: A Dataset and Benchmark for Vision, Tactile, and 3D Point Clouds-based Thin Object Reconstruction

Shania Guo, Yeongsik Seo, Andrew Fu, Mei Hao, Iris Xia, Jiwon Lee, Xinyi Xie, Hyoungseob Park, Aaron Dollar, Alex Wong

State-of-the-art 3D reconstruction models, whether from visual, range, or both, tend to underperform on thin objects. This is partially due to the small amount of space such objects occupy in RGB images and in 3D point clouds. To test the extent of their errors, we collected the first thin object dataset comprising synchronized RGB images, depth maps, and tactile response maps, where each frame is associated with inertial measurements, camera pose and calibration, and groundtruth depth and segmentation maps obtained from laser scanning of thin objects. We hypothesize that tactile data can aid in the reconstruction of thin objects as their response maps provide local shape and deformation information. Our dataset, termed VisTa3D, comprises of 387 scenes covering 70 thin objects over 17 environments. We benchmarked current 3D reconstruction models on VisTa3D and found that, indeed, they exhibit low fidelity on thin objects. To test if tactile data can help, we introduce the first visual-range-tactile 3D reconstruction model as a baseline. Code and data: https://huggingface.co/datasets/shaniaguo/VisTa3D.

ECCV 2026

Y-diff: Structure-Texture Decoupled Diffusion Distillation for H&E-to-pCLE Translation

Haodong Wang, Yan Wen, Hongen Liao, Fang Chen, Tianqi Huang

Probe-based Confocal Laser Endomicroscopy (pCLE) enables real-time in vivo optical biopsies. However, acquiring large-scale, high-quality pCLE images is prohibitively expensive and technically complex, causing severe training data scarcity. Furthermore, when applied to pCLE image generation, existing cross-modal translation models consistently introduce artifacts and cellular structure distortions. To address this, we propose Y-diff, a novel generative framework translating widely accessible H&E-stained pathology images into the scarce pCLE modality with high fidelity, providing robust data support for computational pathology. Y-diff innovatively introduces a knowledge distillation mechanism into this task paradigm, efficiently decoupling the learning of pCLE-domain textures from that of H&E pathology staining domain spatial structures. Specifically, a Teacher model captures distinct pCLE optical textures via color encoding, while a Student model extracts and distills this knowledge under strict H&E spatial constraints. Extensive experiments demonstrate that Y-diff consistently outperforms baselines, while significantly alleviating conventional translation artifacts and structural distortions. What’s more, the synthesized data further expands the scale of this rare modality, reducing mean absolute percentage error to 13.84% in downstream cell counting tasks. Source code is available at: https://github.com/Hdw2agon/Y_diff.

ECCV 2026

Uncertainty-Driven Gaussian Sphere Propagation for 3D Semantic Segmentation

Zhicheng Yan, Qingyong Li, Yixiao Song, Wen Wang

3D point cloud semantic segmentation remains a fundamental challenge for autonomous driving and robotic perception. While Transformerbased architectures have achieved significant progress by capturing longrange dependencies, existing methods rely on deterministic point-wise predictions that fail to maintain semantic consistency across geometric voids such as occlusions and sparse boundaries. This can be attributed to the isotropic nature of conventional discrete feature aggregation, which lacks the directional awareness necessary to propagate reliable information across these discrete gaps. To address this, we propose Uncertainty-driven Gaussian Sphere Propagation (UGSP), a framework that transitions from discrete point processing to continuous geometric field reconstruction. By leveraging approximate Bayesian inference to identify reliable semantic anchors and high-uncertainty voids, a Spherical Harmonics (SH)-based aggregation mechanism is introduced that models the local scene as a collection of anisotropic Gaussian spheres. This approach enables the interpolation of semantic information along continuous spatial paths, allowing for direction-aware semantic propagation that effectively recovers structural integrity in high-uncertainty regions. Extensive experiments on indoor and outdoor benchmarks demonstrate the superiority of UGSP in semantically ambiguous and geometrically complex scenarios. The source code is publicly available at https: //github.com/LENGYI1221/UGSP.

ECCV 2026

Vero: Open Reinforcement Learning Recipes for Visual Reasoning

Gabriel Sarch, Linrong Cai, Qunzhong Wang, Haoyang Wu, Danqi Chen, Zhuang Liu

What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) suggest that broad visual reasoning is within reach, yet their closed data and reinforcement learning (RL) pipelines make their gains difficult to study, reproduce, or extend. We introduce Vero, a family of fully open VLMs that match or exceed existing open-weight models across diverse visual reasoning tasks. We scale RL data and rewards across six broad task categories, constructing Vero-600K, a 600K-sample dataset from 59 datasets, and designing task-routed rewards that handle heterogeneous answers. Across VeroEval, our 30-benchmark suite, Vero-600K outperforms existing RL datasets under controlled comparisons. Applied to five starting models, Vero variants gain 2.9–5.4 points on average over their initial models. Notably, Vero-Qwen3I-8B, trained on the Instruct model, surpasses Qwen3-VL-8B-Thinking by 3.8 points on average without additional distillation. Systematic ablations reveal that different task categories elicit distinct reasoning patterns and that broad gains depend on learning them jointly rather than in isolation. All data, code, and models are released.

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