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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1cb333f0af2d7c952fdb31eddc443480CVF · CVPR 2026

General Process Reward Modeling for Robotic Reinforcement Learning

Tan, Huajie, Chen, Sixiang, Xu, Yijie, Wang, Zixiao, Chi, Cheng, Ji, Yuheng, Lyu, Yaoxu, Zhao, Zhongxia 等 15 位作者

原文摘要

The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promising direction, they are often hindered by two fundamental limitations: their reward models lack step-aware understanding and rely on single-view perception, leading to unreliable assessments of fine-grained manipulation progress; and their reward shaping procedures are theoretically unsound, often inducing a semantic trap that misguides policy optimization. To address these, we introduce Dopamine-Reward, a novel reward modeling method for learning a general-purpose, step-aware process reward model from multi-view inputs. At its core is our General Reward Model (GRM), trained on a vast 3,400+ hour dataset, which leverages Step-wise Reward Discretization for structural understanding and Multi-Perspective Reward Fusion to overcome perceptual limitations. Building upon Dopamine-Reward, we propose Dopamine-RL, a robust policy learning framework that employs a theoretically-sound Policy-Invariant Reward Shaping method, which enables the agent to leverage dense rewards for efficient self-improvement without altering the optimal policy, thereby fundamentally avoiding the semantic trap. Extensive experiments across diverse simulated and real-world tasks validate our approach. GRM achieves state-of-the-art accuracy in reward assessment, and Dopamine-RL built on GRM significantly improves policy learning efficiency. For instance, after GRM is adapted to a new task in a one-shot manner from a single expert trajectory, the resulting reward model enables Dopamine-RL to improve the policy from near-zero to 95% success with only 150 online rollouts (approximately 1 hour of real robot interaction), while retaining strong generalization across tasks.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2a75940ac91223a728c6b98cc6affafcCVF · CVPR 2026

Do You Have Freestyle? Expressive Humanoid Locomotion via Audio Control

Li, Zhe, Chi, Cheng, Wei, Yangyang, Zhu, Boan, Huang, Tao, Sun, Zhenguo, Peng, Yibo, Wang, Pengwei 等 12 位作者

原文摘要

Humans intuitively move to sound, but current humanoid robots lack expressive improvisational capabilities, confined to predefined motions or sparse commands. Generating motion from audio and then retargeting it to robots relies on explicit motion reconstruction, leading to cascaded errors, high latency, and disjointed acoustic-actuation mapping. We propose RoboPerform, the first unified audio-to-locomotion framework that can directly generate music-driven dance and speech-driven co-speech gestures from audio. Guided by the core principle of "motion = content + style", the framework treats audio as implicit style signals and eliminates the need for explicit motion reconstruction. RoboPerform integrates a ResMoE teacher policy for adapting to diverse motion patterns and a diffusion-based student policy for audio style injection. This retargeting-free design ensures low latency and high fidelity. Experimental validation shows that RoboPerform achieves promising results in physical plausibility and audio alignment, successfully transforming robots into responsive freestyle performers capable of reacting to audio.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:444feda5930d2c4699d458b20bae8a25CVF · CVPR 2026

Action-Sketcher: From Reasoning to Action via Visual Sketches for Robotic Manipulation

Tan, Huajie, Co, Peterson, Xu, Yijie, Rong, Shanyu, Ji, Yuheng, Chi, Cheng, Chen, Xiansheng, Zhao, Zhongxia 等 11 位作者

原文摘要

Long-horizon robotic manipulation is increasingly important for real-world deployment, requiring spatial disambiguation in complex layouts and temporal resilience under dynamic interaction. However, existing end-to-end and hierarchical Vision-Language-Action (VLA) policies often rely on text-only cues while keeping plan intent latent, which undermines referential grounding in cluttered or underspecified scenes, impedes effective task decomposition of long-horizon goals with close-loop interaction, and limits causal explanation by obscuring the rationale behind action choices. To address these issues, we first introduce Visual Sketch, an implausible visual intermediate that renders points, boxes, arrows, and typed relations in the robot's current views to externalize spatial intent, connect language to scene geometry, and provide a human-verifiable bridge between high-level reasoning and low-level control. Building on Visual Sketch, we present Action-Sketcher, a VLA framework that operates in a cyclic See -> Think -> Sketch -> Act workflow coordinated by adaptive token-gated strategy for reasoning triggers, sketch revision, and action issuance, thereby supporting reactive corrections and human interaction while preserving real-time action prediction. To enable scalable training and evaluation, we curate diverse corpus with interleaved images, text, Visual Sketch supervision, and action sequences, and train Action-Sketcher with a multi-stage curriculum recipe that combines interleaved sequence alignment for modality unification, language-to-sketch consistency for precise linguistic grounding, and imitation learning augmented with sketch-to-action reinforcement for robustness. Extensive experiments on cluttered scenes and multi-object tasks, in simulation and on real-world tasks, show improved long-horizon success, stronger robustness to dynamic scene changes, and enhanced interpretability via editable sketches and step-wise plans.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:4fd6547d9ffd62391618b24fee5f7393CVF · CVPR 2026

Hierarchical Process Reward Models are Symbolic Vision Learners

Zhang, Shan, Chen, Aotian, Zou, Kai, Gu, Jindong, Xue, Yuan, van den Hengel, Anton

原文摘要

Symbolic computer vision represents diagrams through explicit logical rules and structured representations, enabling interpretable understanding in machine vision. This requires fundamentally different learning paradigms from pixel-based visual models. Symbolic visual learners parse diagrams into geometric primitives--points, lines, and shapes--whereas pixel-based learners operate on textures and colors. We propose a novel self-supervised symbolic auto-encoder, where the encoder parses diagrams into structured primitives and their relations, and the decoder compiles them back into executable code that reproduces the original diagrams. Central to this architecture is SymHPR (Symbolic Hierarchical Process Reward Modeling), which applies hierarchical step-level rewards to enforce point-on-line, line-on-shape, and shape-on-relation consistency. Since reinforcement learning collapses under vanilla GRPO, our proposed stabilized strategies balance exploration and exploitation. Built upon the symbolic learner, we design two pipelines: one replaces the MLLM's visual encoder with the symbolic encoder; the other feeds interpretable symbolic parses directly to the LLM to guide reasoning. Evaluations across reconstruction, perception, and reasoning tasks demonstrate the effectiveness of our approach: achieving a 98.2% reduction in MSE for geometric diagram reconstruction, surpassing GPT-4o by 0.6% with a 7B model on chart reconstruction, and improving by +13% on the MathGlance perception benchmark, and by +3% on MathVerse and GeoQA reasoning benchmarks.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:64a4e8b49f7d869a0f88da1f35c36621CVF · CVPR 2026

GIFT: Global Irreplaceability Frame Targeting for Efficient Video Understanding

Ma, Junpeng, Zhou, Sashuai, Li, Guanghao, Gao, Xin, Cao, Yue, Zeng, Hengyu, Yan, Yuxiang, Wang, Zhibin 等 12 位作者

原文摘要

Video Large Language Models (VLMs) have achieved remarkable success in video understanding, but the significant computational cost from processing dense frames severely limits their practical application. Existing methods alleviate this by selecting keyframes, but their greedy decision-making, combined with a decoupled evaluation of relevance and diversity, often falls into local optima and results in erroneously selecting irrelevant noise frames. To address these challenges, we propose GIFT: Global Irreplaceability Frame Targeting, a novel training-free framework that selects frames by assessing their intrinsic irreplaceability. Specifically, we first introduce Directed Diversity to quantify a frame's uniqueness conditioned on relevance, which allows us to formulate a unified irreplaceability score. Subsequently, our Budget-Aware Refinement strategy employs a adaptive iterative process that first secures a core set of frames with the highest irreplaceability, and then shifts its priority to building crucial temporal context around these selections as the budget expands. Extensive experiments demonstrate that GIFT achieves a maximum average improvement of 12.5% across long-form video benchmarks on LLaVA-Video-7B compared to uniform sampling. Code will be released soon.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6d2e8a2fc1199600f14c65c0db38a770CVF · CVPR 2026

From Manuals to Actions: A Unified VLA Model for Chain-of-Thought Manual Generation and Robotic Manipulation

Gu, Chenyang, Liu, Jiaming, Chen, Hao, Huang, Runzhong, Wuwu, Qingpo, Li, Xiaoqi, Liu, Zhuoyang, Li, Ying 等 12 位作者

原文摘要

Vision-Language-Action (VLA) models have recently emerged, demonstrating strong generalization in robotic scene understanding and manipulation. However, when confronted with long-horizon tasks that require defined goal states, such as LEGO assembly or object rearrangement, existing VLA models still face challenges in coordinating long-horizon planning with precise manipulation.Therefore, we aim to endow a VLA model with the capability to infer the "how" process from the "what" outcomes, transforming goal states into executable procedures. In this paper, we introduce ManualVLA, a unified VLA framework built upon a Mixture-of-Transformers (MoT) architecture, enabling coherent collaboration between multimodal manual generation and action execution. Unlike prior VLA models that directly map sensory inputs to actions, we first equip ManualVLA with a planning expert that generates intermediate manuals consisting of images, visual prompts, and textual instructions. Building upon these multimodal manuals, we design a Manual Chain-of-Thought (ManualCoT) reasoning process that feeds them into the action expert, where each manual step provides explicit control conditions, while its latent representation offers implicit guidance for accurate manipulation. To alleviate the burden of data collection, we develop a high-fidelity digital-twin toolkit based on 3D Gaussian Splatting, which automatically generates manual data for planning expert training. ManualVLA demonstrates strong real-world performance, achieving an average success rate 32% higher than the previous hierarchical SOTA baseline on LEGO assembly and object rearrangement tasks.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:79a35a4306124545250b7c6f204576f9CVF · CVPR 2026

FSLoRA: Harmonizing Detection and Re-Identification via Freq-Spatial Low-Rank Adapter for One-Stage Person Search

Tian, Yanling, Zhang, Shanshan, Chen, Di, Yang, Jian

原文摘要

Person search, which aims to detect and re-identify individuals in unconstrained scenes, faces an inherent conflict in one-stage models: pedestrian detection focuses on shared human features, while person re-identification requires identity-specific representations. Existing approaches, such as feature decoupling and loss re-weighting, primarily address this issue in later network stages but fail to resolve early-stage feature entanglement. To overcome this limitation, we propose FSLoRA, a Freq-Spatial Low-Rank Adapter that progressively decouples task-specific features at the backbone level. FSLoRA consists of a Spatial-Level Module (SLM), which employs LoRA and a mixture-of-experts to dynamically activate task-relevant spatial features, and a Frequency-Level Module (FLM), which transforms features into the frequency domain to selectively enhance task-relevant frequency components while suppressing task-irrelevant noise. By integrating both spatial and frequency-based adaptations, FSLoRA reduces feature interference, enabling more effective joint optimization. Extensive experiments on CUHK-SYSU, PRW, and Posetrack21 demonstrate that FSLoRA not only achieves state-of-the-art performance but also serves as a plug-and-play module adaptable to various person search frameworks, offering a unified and generalizable solution for one-stage person search.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9f96ce3cd90e3fc80830cb93d30df1fdCVF · CVPR 2026

SaPaVe: Towards Active Perception and Manipulation in Vision-Language Action Models for Robotics

Liu, Mengzhen, Zhou, Enshen, Chi, Cheng, Han, Yi, Rong, Shanyu, Chen, Liming, Wang, Pengwei, Wang, Zhongyuan 等 9 位作者

原文摘要

Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven perception actively with robust, viewpoint-invariant execution accordingly. To this end, we propose SaPaVe, an end-to-end framework that jointly learns these capabilities in a data-efficient manner. Central to our approach is a decoupling of camera and manipulation actions, contrary to shared-action-space, and learning in a bottom-up strategy: we first train semantic camera control on our proposed large-scale dataset, then jointly optimizes both action types via hybrid data. To support this, we introduce ActiveViewPose-200K, comprising 200k image-language-camera movement pairs for semantic camera movement learning, and a 3D geometry-aware module that improves execution robustness under dynamic viewpoints. We further present ActiveManip-Bench, the first benchmark filling the gap to evaluate active manipulation. Extensive experiments in both simulation and real-world settings show that SaPaVe outperforms recent VLA models such as GR00T N1 and pi0, achieving up to 31.25% higher success rates in real-world tasks. Our results show that tightly coupled perception and execution, when trained with decoupled yet coordinated strategies, enable efficient and generalizable active manipulation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ade58835ca1025be80bfb2a3178a275eCVF · CVPR 2026

CrossHOI: Learning Cross-View Representations for Monocular 3D Human-Object Interaction Reconstruction

Geng, Pei, Zhang, Shanshan, Yang, Jian

原文摘要

Reconstructing 3D human-object interaction (HOI) from monocular images is highly challenging especially when human and object are mutually occluded. Existing methods primarily rely on single-view inputs, which fundamentally limit their ability to recover occluded regions and accurately estimate contact areas. To address these challenges, we for the first time, consider to introduce novel-view feature priors to enhance monocular 3D HOI reconstruction. We first design a cross-view generator that learns to infer novel-view image features from a single-view input, enriching spatial geometry at the feature level without requiring extra inputs during inference. Guided by both real and generated view features, a spatial cross-view feature fusion module adaptively aggregates complementary cues to enhance the initial reconstruction of human and object meshes. Built upon this reconstruction, we sample 3D vertex features from both views and introduce a bidirectional cross-view Transformer to integrate multi-view vertex representations for accurate contact estimation. Finally, the predicted contact maps are leveraged to refine human-object meshes, yielding geometrically consistent and physically plausible reconstructions.Experiments on BEHAVE and InterCap show that our proposed CrossHOI surpasses state-of-the-art methods in both reconstruction accuracy and contact prediction, especially under severe occlusions.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:b2db9a358ad2e95c5d5c890d4a4b221cCVF · CVPR 2026

ViLoMem: Agentic Learner with Grow-and-Refine Multimodal Semantic Memory

Bo, Weihao, Zhang, Shan, Sun, Yanpeng, Wu, Jingjing, Xie, Qunyi, Tan, Xiao, Chen, Kunbin, He, Wei 等 12 位作者

原文摘要

MLLMs exhibit strong reasoning on isolated queries, yet they operate de novo--solving each problem independently and often repeating the same mistakes. Existing memory-augmented agents mainly store past trajectories for reuse. However, trajectory-based memory suffers from brevity bias, gradually losing essential domain knowledge. More critically, even in truly multimodal problem-solving settings, it records only a **single-modality** trace of past behavior, failing to preserve how visual attention and logical reasoning jointly contributed to the solution. This is fundamentally misaligned with human cognition: semantic memory is both **multimodal and integrated**, preserving visual and abstract knowledge through coordinated but distinct representational streams. We thus introduce **ViLoMem**, a dual-stream memory framework that constructs compact, schema-based memory. It separately encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences. Following a grow-and-refine principle, the system incrementally accumulates and updates multimodal semantic knowledge--preserving stable, generalizable strategies while avoiding catastrophic forgetting. Across nine multimodal benchmarks, **ViLoMem** consistently improves pass@1 accuracy and substantially reduces repeated visual and logical errors. Ablations confirm the necessity of dual-stream memory with explicit distraction-hallucination separation, demonstrating the value of error-aware multimodal memory for lifelong and cross-domain agentic learning.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:beb8f1fd6b945ffc9d9317e1f11b964fCVF · CVPR 2026

GUI-SAGE: Enhancing GUI Automation with Self-Explanatory Learning

Tang, Fei, Gu, Zhangxuan, Lu, Zhengxi, Zhang, Shangzhan, Zeng, Zhengwen, Shen, Shuheng, Meng, Changhua, Yan, Yuchen 等 12 位作者

原文摘要

Reinforcement learning with verifiable rewards (RLVR) has shown promise for GUI automation, enabling agents to learn from binary task completion signals. However, when task difficulty exceeds model capacity, on-policy exploration fails to discover correct actions, creating zero-advantage traps that eliminate learning signals. While incorporating off-policy expert demonstrations seems intuitive, it causes persistent high-entropy states due to distribution mismatch, disrupting effective learning. We propose GUI-SAGE, a self-explanation framework that generates in-distribution reasoning trajectories for GUI automation. By conditioning on ground-truth actions, our method produces in-distribution guidance that avoids the confusion caused by out-of-distribution expert demonstrations. We further introduce Entropy-Modulated Credit Assignment, which recalibrates learning weights by jointly considering prediction confidence and reward signals, enabling amplified updates for confident correct actions and attenuated updates for uncertain explorations. Extensive experiments on AndroidControl and GUI-Odyssey demonstrate that GUI-SAGE-3B achieves competitive performance with 81.1% success rate, substantially outperforming existing methods. Our analysis validates that self-explanations maintain stable learning dynamics while expert demonstrations cause entropy collapse, and that entropy modulation provides the largest improvements on in-distribution samples.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d9093f3aaaa68ae1ec4b047d2315488fCVF · CVPR 2026

VGGT-Ω

Wang, Jianyuan, Chen, Minghao, Zhang, Shangzhan, Karaev, Nikita, Schönberger, Johannes, Labatut, Patrick, Bojanowski, Piotr, Novotny, David 等 10 位作者

原文摘要

We introduce VGGT-Ω, a feed-forward model for 3D reconstruction that improves accuracy, efficiency, and capabilities for both static and dynamic scenes. Prior models such as VGGT have shown that feed-forward 3D reconstruction is, in many cases, competitive with traditional optimization-based methods. Here, we show that the accuracy and robustness of these models scale predictably with model capacity and data size. To enable training 3D reconstruction models at an unprecedented scale, we introduce architectural changes that improve training efficiency and scalability, a high-quality data annotation pipeline that supports dynamic scenes, and a self-supervised learning protocol. We significantly simplify VGGT's architecture by using a single dense prediction head with multi-task supervision, removing expensive high-resolution convolutional layers, and introducing efficient scene tokens for feature aggregation in lieu of global attention. These changes allow us to train VGGT-Ω with 15 x more supervised data than prior work and to leverage vast amounts of unlabeled videos, while requiring only ~30% of VGGT's training memory. VGGT-Ω achieves strong results for 3D reconstruction of static and dynamic scenes across multiple benchmarks, e.g., improving over the previous best camera estimation accuracy by 77% on Sintel.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e14bcb8377007a75fba0361786968caeCVF · CVPR 2026

Revisiting Token Compression for Accelerating ViT-based Sparse Multi-View 3D Object Detectors

Ji, Mingqian, Zhang, Shanshan, Yang, Jian

原文摘要

Vision Transformer (ViT)-based sparse multi-view 3D object detectors have achieved remarkable accuracy but still suffer from high inference latency due to heavy token processing. To accelerate these models, token compression has been widely explored. However, our revisit of existing strategies, such as token pruning, merging, and patch size enlargement, reveals that they often discard informative background cues, disrupt contextual consistency, and lose fine-grained semantics, negatively affecting 3D detection. To overcome these limitations, we propose SEPatch3D, a novel framework that dynamically adjusts patch sizes while preserving critical semantic information within coarse patches. Specifically, we design Spatiotemporal-aware Patch Size Selection (SPSS) that assigns small patches to scenes containing nearby objects to preserve fine details and large patches to background-dominated scenes to reduce computation cost. To further mitigate potential detail loss, Informative Patch Selection (IPS) selects the informative patches for feature refinement, and Cross-Granularity Feature Enhancement (CGFE) injects fine-grained details into selected coarse patches, enriching semantic features. Experiments on the nuScenes and Argoverse 2 validation sets show that SEPatch3D achieves up to 57% faster inference than the StreamPETR baseline and 20% higher efficiency than the state-of-the-art ToC3D-faster, while preserving comparable detection accuracy.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:fccccce0f4c872f041f14580b4b2008dCVF · CVPR 2026

UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying

Bai, Chengyu, Chen, Jintao, Bai, Xiang, Chen, Yilong, She, Qi, Lu, Ming, Zhang, Shanghang

原文摘要

Recent advances in diffusion models and vision-language models (VLMs) have significantly enhanced the controllability of image editing. Methods like FlowEdit enable step-by-step editing along a visible, noise-free trajectory, where each intermediate result is a clear image, eliminating the need for full noise inversion. However, these approaches still operate in pixel space or VAE latent space, where intermediate outputs often suffer from visual artifacts, distortions, or unrealistic details--making reliable semantic evaluation difficult. Furthermore, they remain open-loop systems, applying static edits without feedback to guide or correct the editing process adaptively. We propose UniEdit-I, the first training-free, closed-loop image editing framework that operates entirely within the semantic latent space of a unified VLM by introducing an Understanding-Editing-Verifying (UEV) loop:(1) Understanding: parses the source image and editing instruction into a structured source prompt and a minimal target specification;(2) Editing: applies dynamic semantic offsets, with a configurable feedback weighting mechanism that adaptively modulates editing intensity based on real-time alignment feedback;(3) Verifying: leverages the VLM's own multimodal reasoning capability to evaluate the intermediate output along multiple semantic dimensions and trigger early stopping or refinement. By transforming the VLM from a post-hoc evaluator into an in-process conductor, UniEdit-I establishes the first semantics-driven, self-correcting closed-loop image editing pipeline. Evaluated on GEdit-Bench, UniEdit-I achieves state-of-the-art performance without any fine-tuning or architectural modifications, and even surpasses several large-scale pre-trained editors.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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