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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1df41afc061a3333f21a741dc9c6a19fCVF · CVPR 2026

DreamOmni2: Multimodal Instruction-based Generation and Editing

Xia, Bin, peng, bohao, Zhang, Yuechen, Huang, Junjia, Liu, Jiyang, Li, Jingyao, Tan, Haoru, Wu, Sitong 等 12 位作者

原文摘要

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.

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:5cecb6420f8c9c148f85c4d697bfb852CVF · CVPR 2026

3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience

Xiao, Hongcan, Xiao, Xinyue, Wang, Yilin, Zhang, Yue, Qi, Yonggang

原文摘要

Sketching in 3D space enables expressive reasoning about shape, structure, and spatial relationships, yet generating 3D sketches through natural language remains a major challenge. In this work, we introduce 3DrawAgent, a training-free, language-driven framework for 3D sketch generation that leverages large language models (LLMs) to sequentially draw 3D Bezier curves under geometric feedback. Unlike prior 2D sketch agents, our method introduces a relative experience optimization strategy that adapts the recently proposed Group Reward Policy Optimization (GRPO) paradigm. Instead of relying on explicit ground-truth supervision, we construct pairwise comparisons among generated sketches, with each pair consisting of a relatively better and a worse result based on CLIP-based perceptual rewards and LLM-based fine-grained qualitative assessment. These experiences are then used to iteratively refine the prior knowledge of 3D drawing, enabling black-box reinforcement of the model's 3D awareness. This design allows our model to self-improve its spatial understanding and drawing quality without parameter updates. Experiments show that 3DrawAgent can generate complex and coherent 3D Bezier sketches from diverse textual prompts, exhibit emergent geometric reasoning, and generalize to novel shapes, establishing a new paradigm for advancing the field of training-free 3D sketch intelligence.

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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9a7ed9eb4ed374cfb816000e0ce8b82cCVF · CVPR 2026

Recurrent Reasoning with Vision-Language Models for Estimating Long-Horizon Embodied Task Progress

Zhang, Yuelin, Cheng, Sijie, Li, Chen, Li, Zongzhao, Huang, Yuxin, Liu, Yang, Huang, Wenbing

原文摘要

Accurately estimating task progress is critical for embodied agents to plan and execute long-horizon, multi-step tasks. Despite promising advances, existing Vision-Language Models (VLMs) based methods primarily leverage their video understanding capabilities, while neglecting their complex reasoning potential. Furthermore, processing long video trajectories with VLMs is computationally prohibitive for real-world deployment. To address these challenges, we propose the Recurrent Reasoning Vision-Language Model (\text R ^2VLM). Our model features a recurrent reasoning framework that processes local video snippets iteratively, maintaining a global context through an evolving Chain of Thought (CoT). This CoT explicitly records task decomposition, key steps, and their completion status, enabling the model to reason about complex temporal dependencies. This design avoids the high cost of processing long videos while preserving essential reasoning capabilities. We train \text R ^2VLM on large-scale, automatically generated datasets from ALFRED and Ego4D. Extensive experiments on progress estimation and downstream applications, including progress-enhanced policy learning, reward modeling for reinforcement learning, and proactive assistance, demonstrate that \text R ^2VLM achieves strong performance and generalization, achieving a new state-of-the-art in long-horizon task progress estimation. The models and benchmarks are publicly available at https://huggingface.co/collections/zhangyuelin/r2vlm.

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:ae80ab00eacf65fd65c83590f22d0497CVF · CVPR 2026

RADAR: VQ-VAE Decoder of VAR is a Good Student for Restoring Against Degradation by Acceleration

Wang, Ziyang, Zhang, Yue, Wang, Mingdao, Zhang, Yasen, Song, Teer, Tian, Yu, Li, Xueming

原文摘要

Visual Autoregressive Modeling (VAR) has recently emerged as a powerful paradigm for image generation that surpasses diffusion models in efficiency and quality. However, accelerating attention computation in VAR is still challenging because attention patterns across scales exhibit strong and complex semantic biases that early coarse-scale tokens dominate global structure, while fine-scale tokens mainly refine local details. Existing acceleration methods rely on heuristic token pruning or fixed attention masks, lacking a principled way to balance acceleration and semantic fidelity. In this work, we propose a two-stage acceleration framework for VAR. First, we introduce a semantic-cost-aware masking strategy (SCA-Mask) that quantifies the importance of each attention tile and formulates mask shape design as a cost-constrained optimization problem. This enables adaptive pruning under a given compute budget while preserving essential semantic context. Second, we present Post-Acceleration Adaptation (PAA), a decoder-side fine-tuning scheme that employs internal knowledge distillation to restore image quality from pruned latents. PAA does not require external data and uses a lightweight LoRA-based adaptation, providing a highly efficient alternative to retraining the autoregressive transformer. Comprehensive experiments across multiple VAR tasks demonstrate that our method achieves decent speedup with negligible loss of visual fidelity, yielding a principled and effective pathway toward fast and high-quality visual autoregressive generation.

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:b18f486952070c46f671b73ec6b7494aCVF · CVPR 2026

Evo-Retriever: LLM-Guided Curriculum Evolution with Viewpoint-Pathway Collaboration for Multimodal Document Retrieval

Li, Weiqing, Guo, Jinyue, Wang, Yaqi, Xiao, Haiyang, Zhang, Yuewei, Liu, Guohua, Wang, Hao Henry

原文摘要

Visual-language models (VLMs) excel at data mappings, but real-world document heterogeneity and unstructuredness disrupt the consistency of cross-modal embeddings. Recent late-interaction methods enhance image-text alignment through multi-vector representations, yet traditional training with limited samples and static strategies cannot adapt to the model's dynamic evolution, causing cross-modal retrieval confusion. To overcome this, we introduce Evo-Retriever, a retrieval framework featuring an LLM-guided curriculum evolution built upon a novel Viewpoint-Pathway collaboration. First, we employ multi-view image alignment to enhance fine-grained matching via multi-scale and multi-directional perspectives. Then, a bidirectional contrastive learning strategy generates "hard queries" and establishes complementary learning paths for visual and textual disambiguation to rebalance supervision. Finally, the model-state summary from the above collaboration is fed into an LLM meta-controller, which adaptively adjusts the training curriculum using expert knowledge to promote the model's evolution. On ViDoRe V2 and MMEB (VisDoc), Evo-Retriever achieves state-of-the-art performance, with nDCG@5 scores of 65.2 % and 77.1 %.

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:c11c2b2f7d34f550a55ee6de62fbfd0eCVF · CVPR 2026

UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation

Huang, Jiehui, Zhang, Yuechen, He, Xu, Gao, Yuan, Cen, Zhi, Xia, Bin, Zhou, Yan, Tao, Xin 等 10 位作者

原文摘要

Recent video generation models demonstrate impressive synthesis capabilities but remain limited by single-modality conditioning, constraining their holistic world understanding. This stems from insufficient cross-modal interaction and limited modal diversity for comprehensive world knowledge representation.To address these limitations, we introduce UnityVideo, a unified framework for world-aware video generation that jointly learns across multiple modalities--segmentation masks, human skeletons, DensePose, optical flow, and depth maps--and training paradigms. Our approach features two core components: (1) dynamic noising to unify heterogeneous training paradigms, and (2) a modality switcher with an in-context learner that enables unified processing via modular parameters and contextual learning. We contribute a large-scale unified dataset with 1.3M samples. Through joint optimization, UnityVideo accelerates convergence and significantly enhances zero-shot generalization to unseen data. We demonstrate that UnityVideo achieves superior video quality, consistency, and improved alignment with physical world constraints.

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:d8e175ac9a57e6ba2365d6257d11c274CVF · CVPR 2026

Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration

Wu, Chen, Wang, Ling, Zheng, Zhuoran, Cui, Yuning, Yang, Zhixiong, Chen, Xiangyu, Zhang, Yue, Jiang, Weidong 等 9 位作者

原文摘要

Ultra-High-Definition (UHD) image restoration is trapped in a scalability crisis: existing models, bound to pixel-wise operations, demand unsustainable computation. While state space models (SSMs) like Mamba promise linear complexity, their pixel-serial scanning remains a fundamental bottleneck for the millions of pixels in UHD content. We ask: must we process every pixel to understand the image? This paper introduces C^2SSM, a visual state space model that breaks this taboo by shifting from pixel-serial to cluster-serial scanning. Our core discovery is that the rich feature distribution of a UHD image can be distilled into a sparse set of semantic centroids via a neural-parameterized mixture model. C^2SSM leverages this to reformulate global modeling into a novel dual-path process: it scans and reasons over a handful of cluster centers, then diffuses the global context back to all pixels through a principled similarity distribution, all while a lightweight modulator preserves fine details. This cluster-centric paradigm achieves a decisive leap in efficiency, slashing computational costs while establishing new state-of-the-art results across five UHD restoration tasks. More than a solution, C^2SSM charts a new course for efficient large-scale vision: scan clusters, not pixels. The code is available at https://github.com/5chen/C2SSM.

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:dba75a2d6cf90ef72d3e49577a07dd26CVF · CVPR 2026

Beyond Mimicry: Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations

Huang, Wei-Jin, Zhang, Yue-Yi, Wei, Yi-Lin, Xia, Zhi-Wei, Tan, Juantao, Li, Yuan-Ming, Zhao, Zhilin, Zheng, Wei-Shi

原文摘要

Enabling humanoid robots to physically interact with humans is a critical frontier, but progress is hindered by the scarcity of high-quality Human-Humanoid Interaction (HHoI) data. While leveraging abundant Human-Human Interaction (HHI) data presents a scalable alternative, we first demonstrate that standard retargeting fails by breaking the essential contacts. We address this with PAIR (Physics-Aware Interaction Retargeting), a contact-centric, two-stage pipeline that preserves contact semantics across morphology differences to generate physically consistent HHoI data.This high-quality data, however, exposes a second failure: conventional imitation learning policies merely mimic trajectories and lack interactive understanding. We therefore introduce D-STAR (Decoupled Spatio-Temporal Action Reasoner), a hierarchical policy that disentangles when to act from where to act. In D-STAR, Phase Attention (when) and a Multi-Scale Spatial module (where) are fused by the diffusion head to produce synchronized whole-body behaviors beyond mimicry.By decoupling these reasoning streams, our model learns robust temporal phases without being distracted by spatial noise, leading to responsive, synchronized collaboration. We validate our framework through extensive and rigorous simulations, demonstrating significant performance gains over baseline approaches and a complete, effective pipeline for learning complex whole-body interactions from HHI data.

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:dcd5de55ff95002bb6d9592c7ccb2d6fCVF · CVPR 2026

PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks

Cui, Cheng, Zhang, Yubo, Sun, Ting, Wang, Xueqing, Liu, Hongen, Lin, Manhui, Zhang, Yue, Gao, Tingquan 等 14 位作者

原文摘要

The advent of "OCR 2.0" and large-scale vision-language models (VLMs) has set new benchmarks in text recogni- tion. However, these unified architectures often come with significant computational demands, challenges in precise text localization within complex layouts, and a propen- sity for textual hallucinations. Revisiting the prevailing notion that model scale is the sole path to high accu- racy, this paper introduces PP-OCRv5, a meticulously op- timized, lightweight OCR system with merely 5 million pa- rameters. We demonstrate that PP-OCRv5 achieves per- formance competitive with many billion-parameter VLMs on standard OCR benchmarks, while offering superior lo- calization precision and reduced hallucinations. The cor- nerstone of our success lies not in architectural expansion but in a data-centric investigation. We systematically dis- sect the role of training data by quantifying three critical dimensions: data difficulty, data accuracy, and data diver- sity. Our extensive experiments reveal that with a sufficient volume of high-quality, accurately labeled, and diverse data, the performance ceiling for traditional, efficient two- stage OCR pipelines is far higher than commonly assumed. This work provides compelling evidence for the viability of lightweight, specialized models in the large-model era and offers practical insights into data curation for OCR. The source code and models are publicly available at https: //github.com/PaddlePaddle/PaddleOCR.

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:f036c6ca82d59c93ec0eb9ef8183c3b6CVF · CVPR 2026

Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

Cui, Cheng, Sun, Ting, Liang, Suyin, Gao, Tingquan, Zhang, Zelun, Liu, Jiaxuan, Wang, Xueqing, Zhou, Changda 等 18 位作者

原文摘要

Document parsing is a fine-grained task where image resolution significantly impacts performance. While advanced research leveraging vision-language models benefits from high-resolution input to boost model performance, this often leads to a quadratic increase in the number of vision tokens and significantly raises computational costs. We attribute this inefficiency to substantial visual regions redundancy in document images, like background. To tackle this, we propose PaddleOCR-VL, a novel coarse-to-fine architecture that focuses on semantically relevant regions while suppressing redundant ones, thereby improving both efficiency and performance. Specifically, we introduce a lightweight Valid Region Focus Module (VRFM) which leverages localization and contextual relationship prediction capabilities to identify valid vision tokens. Subsequently, we design and train a compact yet powerful 0.9B vision-language model (PaddleOCR-VL-0.9B) to perform detailed recognition, guided by VRFM outputs to avoid direct processing of the entire large image. Extensive experiments demonstrate that PaddleOCR-VL achieves state-of-the-art performance in both page-level parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference while utilizing substantially fewer vision tokens and parameters, highlighting the effectiveness of targeted coarse-to-fine parsing for accurate and efficient document understanding. The source code and models are publicly available at https://github.com/PaddlePaddle/PaddleOCR.

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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