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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3efdd67a3c1a6b556dfabff44991fc35CVF · CVPR 2026

SketchRevive: Fine-Grained Pixel-to-Vector Sketch Completion with Diffusion-Prior-Guided Multimodal LLMs

Zuo, Ran, Hu, Haoxiang, Pei, Chenxi, Liu, Yanxuan, Qiang, Wenwen, Liu, Fang, Deng, Xiaoming, Ma, Cuixia 等 9 位作者

原文摘要

Transforming sparse, partial pixel sketches from diverse media into complete, editable vector drawings is essential yet underexplored in digital creation. Prior methods either generate from scratch or inpaint local gaps without predicting global structure, leading to coarse contours and limited detail. To address this, we introduce SketchRevive, a two-stage framework for fine-grained pixel-to-vector sketch completion that couples diffusion-based pixel completion with MLLM-driven refinement and vectorization to produce coherent, detail-faithful SVG results. Specifically, we first construct a practical benchmark by augmenting stroke-annotated sketches from paper and whiteboards. Stage I trains a diffusion model with a line-distribution head to predict per-pixel stroke presence, producing structurally and visually consistent completions. Stage II fine-tunes an MLLM for structure-aware SVG vectorization with iterative refinement, optimized by instance-level stroke attribute similarities. To align key clues, e.g., spatial structure, appearance details across both stages, we introduce a diffusion-prior aggregated encoding module by injecting multi-scale UNet features from Stage I into the MLLM's visual embeddings and using line prediction logits for token compression to prioritize informative tokens. Experiments indicate that SketchRevive completes topology-coherent vector outputs with high fidelity and recognizability while preserving user intent, suitable for interactive creation and artistic design.

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:43b788bdd833be0049c009bf1ab699a0CVF · CVPR 2026

ORSATR-X: A Foundation Model based on Differential-and-Excitation Networks for Optical Remote Sensing Object Recognition

Mo, Canyu, Liu, Yongxiang, Zhang, Jiehua, Yu, Zilong, Liu, Zhen, Liu, Tianpeng, Liu, Li

原文摘要

Recent advances in Remote Sensing Foundation Models (RSFMs) have demonstrated considerable potential for Earth Observation (EO) tasks. While adopting natural image foundation models (e.g., DINO) provides a data-efficient strategy for building RSFMs, their strong generalization capability does not fully transfer to complex remote sensing (RS) scenarios due to severe background interference, notably in perceiving challenging targets like low-contrast objects. To this end, we propose ORSATR-X, a novel RSFM that effectively integrates the generalizable representations of DINOv3 with a dedicated mechanism for exciting local contrast information. ORSATR-X comprises two core components: (1) a DINOv3 encoder, which provides rich feature representation under limited RS pre-training data, and (2) a carefully designed side network incorporating a Weber Local Adapter (WLA) and a Multi-scale Aggregation Module (MSAM). The WLA enhances discriminability of low-contrast boundaries in complex scenes through center-surround contrast and directional gradient information enhancement, while the MSAM handles inherent object scale variations in RS imagery by adaptive aggregation of features across multiple scales. Furthermore, we pre-train the side network using an efficient self-supervised distillation strategy. Extensive experiments on scene classification, object detection, and semantic segmentation demonstrate that ORSATR-X achieves state-of-the-art performance among existing RSFMs, demonstrating the effectiveness of our design.

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:47060b8ce6286103a624d4d92f100912CVF · CVPR 2026

PET-DINO: Unifying Visual Cues into Grounding DINO with Prompt-Enriched Training

Fu, Weifu, Li, Jinyang, Gao, Bin-Bin, Li, Jialin, Lin, Yuhuan, Deng, Hanqiu, Tao, Wenbing, Liu, Yong 等 9 位作者

原文摘要

Open-Set Object Detection (OSOD) enables recognition of novel categories beyond fixed classes but faces challenges in aligning text representations with complex visual concepts and the scarcity of image-text pairs for rare categories. This results in suboptimal performance in specialized domains or with complex objects. Recent visual-prompted methods partially address these issues but often involve complex multi-modal designs and multi-stage optimizations, prolonging the development cycle. Additionally, effective training strategies for data-driven OSOD models remain largely unexplored. To address these challenges, we propose PET-DINO, a universal detector supporting both text and visual prompts. Our Alignment-Friendly Visual Prompt Generation (AFVPG) module builds upon an advanced text-prompted detector, addressing the limitations of text representation guidance and reducing the development cycle. We introduce two prompt-enriched training strategies: Intra-Batch Parallel Prompting (IBP) at the iteration level and Dynamic Memory-Driven Prompting (DMD) at the overall training level. These strategies enable simultaneous modeling of multiple prompt routes, facilitating parallel alignment with diverse real-world usage scenarios. Comprehensive experiments demonstrate that PET-DINO exhibits competitive zero-shot object detection capabilities across various prompt-based detection protocols. These strengths can be attributed to inheritance-based philosophy and prompt-enriched training strategies, which play a critical role in building an effective generic object detector. Project page: https://fuweifuvtoo.github.io/pet-dino.

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:4ad5b308dd0f408bc9f4cb3f75d06be2CVF · CVPR 2026

Rotation Invariant and Symmetry Aware Pixel Difference Network for Remote Sensing Object Detection

Zhan, Jialei, Liu, Li, Zhang, Jiehua, Xie, Yuhang, Liu, Yongxiang, Chen, Jiangming, Cheng, Ming-Ming

原文摘要

Recent advancements in remote sensing object detection have predominantly focused on oriented bounding box design and small object feature enhancement, while often overlooking the intrinsic geometric properties of remote sensing images, such as rotation invariance and structural symmetry. Many aerial objects appear in multiple orientations and exhibit clear symmetrical patterns, which, if not explicitly modeled, can lead to detection failures and inaccurate localization under geometric variation or partial occlusion. To address this, we propose the Rotation Invariant and Symmetry Aware Pixel Difference Network (RIS-PiDiNet), which introduces a novel convolutional operator called Rotation Invariant and Symmetry Aware Pixel Difference Convolution (RIS-PDC). This operator replaces traditional convolution with a mathematically grounded formulation that encodes rotation group priors and symmetrical constraints. RIS-PDC utilizes pixel differences and symmetry-guided aggregation in the polar harmonic space, enabling the network to infer partially visible structures and deduce occluded symmetrical parts. Besides improving detection accuracy, RIS-PDC enhances model interpretability by embedding geometric principles into the network design. Feature visualizations demonstrate rotation-consistent activations and symmetry-complete responses, revealing how the network captures underlying object structure even under partial visibility or orientation changes. This yields geometrically interpretable detection decisions. To our knowledge, RIS-PiDiNet is the first remote sensing object detection framework that jointly incorporates rotation invariance and symmetry modeling within a unified architecture. Extensive evaluations on standard benchmarks validate its effectiveness, achieving state-of-the-art performance on DOTA-v1.0 (78.53% mAP single-scale, 81.81% multi-scale), HRSC2016 (98.60% mAP), and DIOR-R (67.28% mAP), all with acceptable computational overhead and no increase in parameter count.

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:742814f7817c0c8a42dd36110ce700f8CVF · CVPR 2026

SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training

Wang, Mengmeng, Jiang, Dengyang, Li, Liuzhuozheng, Lin, Yucheng, Shen, Guojiang, Kong, Xiangjie, Liu, Yong, Dai, Guang 等 9 位作者

原文摘要

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as REPA (relying on external representation encoders) or SRA (requiring dual-model setups), inevitably incur heavy computational overhead during training due to external dependencies. To tackle these challenges, this paper proposes SRA 2, a lightweight intrinsic self-representation alignment framework for efficient diffusion training. SRA 2 leverages off-the-shelf pre-trained Variational Autoencoder (VAE) features: their reconstruction property ensures inherent encoding of visual priors like rich texture details, structural patterns, and basic semantic information. Specifically, SRA 2 aligns the intermediate latent features of diffusion transformers with VAE features via a lightweight projection layer, supervised by a feature alignment loss. This design accelerates training without extra representation encoders or dual-model maintenance, resulting in a simple yet effective pipeline. Extensive experiments demonstrate that SRA 2 improves both generation quality and training convergence speed compared to vanilla diffusion transformers, matches or outperforms state-of-the-art acceleration methods, and incurs merely 4% extra GFLOPs with zero additional cost for external guidance models.

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:7a8d117b3f313db2e94bd95eac32a0f2CVF · CVPR 2026

DynBridge: Bridging Imagination and Control through Interaction Dynamics for Robot Manipulation

Wang, Alex, Dong, Zhiwei, Bai, Qicheng, Zhang, Chenshi, Yi, Yujie, Dai, Guang, Liu, Yong, Wang, Mengmeng

原文摘要

Recent generative models allow robots to generate future visual outcomes for action guidance, yet most still address imagination and control independently, resulting in visually coherent rollouts but physically inconsistent behaviors. While structural priors enhance spatial grounding, these methods remain visually correlation-driven rather than causally informed, overlooking the bidirectional coupling between robot actions and the evolving environment. We formalize the coupling as interaction dynamics, which specify where environmental changes occur and how actions cause them. Based on this formulation, we introduce DynBridge, an end-to-end framework that unifies imagination and control through the shared dynamics representation. Specifically, DynBridge realizes this via three components: (1) an Interaction Dynamics Generator that forecasts interaction dynamics via joint trajectory generation and action prediction; (2) an Action-Conditioned Dynamics Aggregator that integrates dynamics under control signals; and (3) a Dynamics-Guided Action Predictor that leverages the aggregated dynamics to produce executable, context-aware actions. Results demonstrate that DynBridge consistently outperforms prior methods on simulated and real-world benchmarks without external pretraining.

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:863827d498ba14535fff47849d0be0d4CVF · CVPR 2026

Diagram2Structure: Unlocking LLMs' Diagram Comprehension through DiagramDiff, a Framework for Structuring Offline Diagrams

Hu, Haoxiang, Li, Yaokun, Huang, Zeyuan, Gao, Cangjun, He, Qiang, Li, Qingkun, Deng, Xiaoming, Ma, Cuixia 等 11 位作者

原文摘要

Diagrams are widely used in daily life. However, offline diagrams typically exist in the form of images, lacking structured data representation, which significantly limits their reusability and editability. Current research mainly focuses on supporting basic query tasks for online diagrams and does not meet the semantic understanding and interaction requirements for complex offline diagrams. Although large language models (LLMs) possess powerful reasoning and knowledge integration capabilities, their performance in processing offline diagrams is unsatisfactory due to the inability to accurately understand the structure and content of offline diagrams. To address these issues,we propose DiagramDiff, a framework consisting of a high-precision diagram reconstruction model and an instance-level diagram element recognition model. The framework converts offline diagrams into standardized data structures, enabling LLMs to transition from being unable to understand offline diagrams to becoming intelligent assistants capable of performing tasks such as semantic reasoning, logical validation, and efficient diagram editing. We have constructed a dataset containing diagrams and their corresponding question and answering(Q&A) and editing tasks. Experiments demonstrate that DiagramDiff achieves state-of-the-art performance in diagram reconstruction and recognition tasks, significantly enhancing LLMs' understanding and interaction capabilities with offline diagrams.

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:870539573de1e3692f62b6f11209cbd0CVF · CVPR 2026

ClimaOoD: Improving Anomaly Segmentation via Physically Realistic Synthetic Data

Liu, Yuxing, Li, Zheng, Liang, Huanhuan, Zhang, Ji, Sun, Zeyu, Liu, Yong

原文摘要

Anomaly segmentation seeks to detect and localize unknown or out-of-distribution (OoD) objects that fall outside predefined semantic classes--a capability essential for safe autonomous driving. However, the scarcity and limited diversity of anomaly data severely constrain model generalization in open-world environments. Existing approaches mitigate this issue through synthetic data generation, either by copy-pasting external objects into driving scenes or by leveraging text-to-image diffusion models to inpaint anomalous regions. While these methods improve anomaly diversity, they often lack contextual coherence and physical realism, resulting in domain gaps between synthetic and real data. In this paper, we present ClimaDrive, a semantics-guided image-to-image framework for synthesizing semantically coherent, weather-diverse, and physically plausible OoD driving data. ClimaDrive unifies structure-guided multi-weather generation with prompt-driven anomaly inpainting, enabling the creation of visually realistic training data. Based on this framework, we construct ClimaOoD, a large-scale benchmark spanning six representative driving scenarios under both clear and adverse weather conditions.Extensive experiments on four state-of-the-art methods show that training with ClimaOoD leads to robust improvements in anomaly segmentation. Across all methods, AUROC, AP, and FPR95 show notable gains, with FPR95 dropping from 3.97 to 3.52 for RbA on Fishyscapes LAF. These results demonstrate that ClimaOoD enhances model robustness, offering valuable training data for better generalization in open-world anomaly detection.

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:919b2a43679bfad62937c90a4e987b10CVF · CVPR 2026

RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction

Peng, Chenxu, Wang, Chenxu, Dai, Yimian, Liu, Yongxiang, Cheng, Ming-Ming, Li, Xiang

原文摘要

Accurate road segmentation from aerial imagery is fundamental to many geospatial applications. However, existing datasets often suffer from limited scene diversity, low semantic granularity, and poor structural continuity, restricting their generalization across environments. To address these challenges, we introduce WorldRoadSeg-360K, the largest and most diverse road segmentation dataset to date, comprising 366,947 high-resolution images collected from 38 countries and 223 cities across various terrains and continents. WorldRoadSeg-360K serves as a comprehensive benchmark and reveals key challenges in handling diverse and structurally complex scenes. Automated approaches often struggle to preserve road connectivity, while current interactive methods lack efficient, topology-sensitive tools for real-world road editing. To this end, we present RoadGIE, establishing a novel interactive paradigm for road extraction in remote sensing. Unlike prior point- or box-based prompting strategies, RoadGIE supports connectivity-aware prompts, including clicks and scribbles, which inherently align with the topology of road networks. To improve structural consistency and mitigate performance degradation during iterative interactions, RoadGIE integrates an expert-guided prompting strategy and adapts the skeleton-based recall loss for interactive scenarios. Meanwhile, to alleviate user intent ambiguity, RoadGIE introduces a topo-semantic instantiation during training to enhance interaction stability and consistency. RoadGIE achieves state-of-the-art performance in both segmentation accuracy and topological consistency on WorldRoadSeg-360K and other benchmarks, while maintaining efficient operation with only 3.7 million parameters and real-time processing capabilities.

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:ae3d8fcff267e5b5ff38fc3b60de2676CVF · CVPR 2026

Rethinking Knowledge Transfer in Image Quality Assessment: A Perceptual Preference Structure Alignment Perspective

Li, Aobo, Wu, Jinjian, Liu, Yongxu, Ma, Jupo, Dong, Weisheng

原文摘要

As imaging scenarios diversify rapidly, Image Quality Assessment (IQA) faces a key challenge: how to effectively transfer perceptual knowledge from existing annotated datasets to ensure reliable quality prediction in new scenarios. However, current IQA models struggle to generalize: direct transfer often leads to severe performance degradation, while multi-dataset joint training rarely yields stable gains and can even harm target performance. In this work, we identify perceptual preference structure mismatch as an important and underexplored factor behind transfer failure in IQA. Models trained on different source datasets may rely on different perceptual cues, leading to discrepancies in the conditional distribution P(Y|X) that hinder effective transfer. To address this, we propose Perceptual Preference Representation (PPR), which characterizes dataset-specific perceptual preference structures by analyzing correlations between visual features and quality scores. Based on PPR, we define Perceptual Preference Consistency (PPC), a training-free and interpretable measure of relative preference compatibility across datasets. Building on this, we develop Preference-Structure-Aligned Transfer (PreSTA), which selects source samples better aligned with the target domain in terms of perceptual preference structure. Extensive experiments show that PreSTA achieves superior performance across cross-domain, within-domain, and targeted joint transfer settings while using only a small fraction of source data. These results highlight that aligning perceptual preference structures, rather than simply increasing dataset size, is crucial for effective and data-efficient knowledge transfer in IQA. Code will be publicly available at https://github.com/Li-aobo/PreSTA.

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:b9e00dcb1da2e19d57250e2adc5597d9CVF · CVPR 2026

StreamAvatar: Streaming Diffusion Models for Real-Time Interactive Human Avatars

Sun, Zhiyao, Peng, Ziqiao, Ma, Yifeng, Chen, Yi, Zhou, Zhengguang, Zhou, Zixiang, Zhang, Guozhen, Zhang, Youliang 等 11 位作者

原文摘要

Real-time, streaming interactive avatars represent a critical yet challenging goal in digital human research. Although diffusion-based human avatar generation methods achieve remarkable success, their non-causal architecture and high computational costs make them unsuitable for streaming. Moreover, existing interactive approaches are typically restricted to the head-and-shoulder region, limiting their ability to produce gestures and body motions. To address these challenges, we propose a two-stage autoregressive adaptation and acceleration framework that applies autoregressive distillation and adversarial refinement to adapt a high-fidelity human video diffusion model for real-time, interactive streaming. To ensure long-term stability and consistency, we introduce three key components: a Reference Sink, a Reference-Anchored Positional Re-encoding (RAPR) strategy, and a Consistency-Aware Discriminator. Building on this framework, we develop a one-shot, interactive, human avatar model capable of generating both natural talking and listening behaviors with coherent gestures. Extensive experiments demonstrate that our method achieves state-of-the-art performance, surpassing existing approaches in generation quality, real-time efficiency, and interaction naturalness.

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 ↗