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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bdd9898dfae567d4d452217a0f22dc96CVF · CVPR 2026

MV-RoMa: From Pairwise Matching into Multi-View Track Reconstruction

Lee, Jongmin, Kang, Seungyeop, Yoo, Sungjoo

原文摘要

Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner, often producing fragmented and geometrically inconsistent tracks when their predictions are chained across views. We propose MV-RoMa, a multi-view dense matching model that jointly estimates dense correspondences from a source image to multiple co-visible targets. Specifically, we design an efficient model architecture which avoids high computational cost of full cross-attention for multi-view feature interaction: (i) multi-view encoder that leverages pair-wise matching results as a geometric prior, and (ii) multi-view matching refiner that refines correspondences using pixel-wise attention. Additionally, we propose a post-processing strategy that integrates our model's consistent multi-view correspondences as high-quality tracks for SfM. Across diverse and challenging benchmarks, MV-RoMa produces more reliable correspondences and substantially denser, more accurate 3D reconstructions than existing sparse and dense matching methods.

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:bdefca5a941b6501c3e997151afcd8bbCVF · CVPR 2026

UniCorrn: Unified Correspondence Transformer Across 2D and 3D

Goswami, Prajnan, Ding, Tianye, Liu, Feng, Jiang, Huaizu

原文摘要

Visual correspondence across image-to-image (2D-2D), image-to-point cloud (2D-3D), and point cloud-to-point cloud (3D-3D) geometric matching forms the foundation for numerous 3D vision tasks. Despite sharing a similar problem structure, current methods use task-specific designs with separate models for each modality combination. We present UniCorn, the first correspondence model with shared weights that unifies geometric matching across all three tasks. Our key insight is that Transformer attention naturally captures cross-modal feature similarity. We propose a dual-stream decoder that maintains separate appearance and positional feature streams. This design enables end-to-end learning through stack-able layers while supporting flexible query-based correspondence estimation across heterogeneous modalities. Our architecture employs modality-specific backbones followed by shared encoder and decoder components, trained jointly on diverse data combining pseudo point clouds from depth maps with real 3D correspondence annotations. UniCorn achieves competitive performance on 2D-2D matching and surpasses prior state-of-the-art by 8% on 7Scenes (2D-3D) and 10% on 3DLoMatch (3D-3D) in registration recall. Code is provided at \href https://github.com/neu-vi/UniCorrn github.com/neu-vi/UniCorrn .

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:bdf9bc262ad622f82035d52c2007ca8cCVF · CVPR 2026

VES-RFT: Rewarding Visual Evidence Sensitivity to Mitigate Hallucinations in Large Vision-Language Models

Hou, Xuehe, Li, Wenshuo, Li, Yali, Shu, Han, Wang, Yuan, Chen, Xinghao, Wang, Shengjin

原文摘要

Vision-Language Models (VLMs) often over-rely on linguistic priors even when images are provided, leading to object hallucinations. We revisit object-wise hallucination from the perspective of how visual evidence shapes the model's uncertainty. For each input, we measure decision uncertainty with and without the image, and define a Visual Evidence Sensitivity (VES) signal as the image-attributable change in entropy. Building on this signal, we introduce Visual Evidence Sensitivity Reinforcement Fine-Tuning (VES-RFT), a training-time reinforcement fine-tuning method that explicitly rewards reliance on correct visual evidence. We pair this continuous, annotation-free signal with a verifiable reward that enforces factual object correctness by automatically checking generated object mentions against the image, yielding a computable objective without human annotations. We optimize the dual objective using critic-free GRPO with KL regularization, requiring only parallel image and no-image passes during training while preserving single-pass inference. Across multiple VLM families and benchmarks, VES-RFT consistently suppresses object hallucinations and improves robustness under ambiguity. Specifically, on LLaVA-7B, VES-RFT reduces 12.8 and 1.8 on CHAIR_S/CHAIR_I of MS-COCO, and increases POPE accuracy by 4.92%. Extensive experiments indicate that turning uncertainty into a learnable reward, paired with verifiable correctness signals, provides a scalable mechanism for training-time hallucination mitigation and stronger visual grounding.

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:bdfd472a4e748667038e57cdd91c9fe5CVF · CVPR 2026

MR. Illuminate: Zero-Shot Low-Light Image Enhancement with Diffusion Prior

Cho, Joshua, Aghajanzadeh, Sara, Zhu, Zhen, Forsyth, David

原文摘要

The primary axes of interest in low-light image enhancement (LLIE) are color constancy--ensuring consistent outputs across inputs of the same scene under varying illumination and noise--and generalization across diverse datasets. Existing methods, whether supervised, unsupervised, or zero-shot, rely on auxiliary loss functions and empirically selected hyperparameters, which yield strong results on the datasets used for evaluation but often exhibit limited generalization. To overcome these constraints, we propose MR. Illuminate (pronounced "Mister Illuminate"), the first deep learning-based solution for LLIE that requires no optimization and no degradation assumption. "MR." emphasizes our Modulate-Refine design: global illuminance and color are modulated via Adaptive Instance Normalization (AdaIN), while local structure and color are refined through self-attention features within a pre-trained diffusion model, taking a unique approach from prior methods. Extensive quantitative evaluations show that our approach surpasses SOTA methods on standard LLIE benchmarks, while qualitative results demonstrate improved color fidelity. Moreover, without any modification to our framework, our method achieves competitive results on the auto white balance (AWB) task, underscoring its strong generalization capability.

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:be00105c0b0189b90076604c4a990a5eCVF · CVPR 2026

EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors

Bartolomei, Luca, Tosi, Fabio, Poggi, Matteo, Mattoccia, Stefano, Gallego, Guillermo

原文摘要

We propose EventHub, a novel framework for training deep-event stereo networks without ground truth annotations from costly active sensors, relying instead on standard color images. From these images, we derive either proxy annotations and proxy events through state-of-the-art novel view synthesis techniques, or simply proxy annotations when images are already paired with event data. Using the training set generated by our data factory, we repurpose state-of-the-art stereo models from RGB literature to process event data, obtaining new event stereo models with unprecedented generalization capabilities.Experiments on widely used event stereo datasets support the effectiveness of EventHub and show how the same data distillation mechanism can improve the accuracy of RGB stereo foundation models in challenging conditions such as nighttime scenes.

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:be031000e1b20fcf5e4de1e1303d911eCVF · CVPR 2026

Pantheon360: Taming Digital Twin Generation via 3D-Aware 360deg Video Diffusion

Chen, Ting-Hsuan, Chen, Ying-Huan, Tu, Tao, Lee, Jie-Ying, Wu, Cho-Ying, Lin, Fangzhou, Zhang, Hengyuan, Paz, David 等 13 位作者

原文摘要

Generating complete digital twins from videos requires precise camera control, global scene coverage, and strict spatial-temporal consistency--constraints that remain challenging for perspective video generators due to their limited field of view (FoV). Their narrow FoV forces long or multi-view trajectories, amplifying cross-view inconsistency and temporal drift.We argue that 360deg video generation offers a natural solution: panoramic coverage simplifies trajectory design and provides strong global context for maintaining coherence. We introduce Pantheon360: Taming Digital Twin Generation via 3D-Aware 360deg Video Diffusion, a controllable 360deg video generation framework that synthesizes high-fidelity videos from sparse 360deg inputs. The key idea is an explicit 3D Cache, reconstructed from the input, which serves as a geometric scaffold for any user-defined camera path. This allows the diffusion model to focus on photorealistic texture refinement while the 3D Cache enforces global geometric consistency.Experiments show that Pantheon360 achieves superior visual quality and unmatched geometric coherence, enabling reliable and flexible 360deg scene generation for downstream simulation and digital-twin applications.

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:be075e08ca7620194f237b23b1981dd6CVF · CVPR 2026

Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models

Shen, Zhirong, Huang, Rui, Liu, Jiacheng, Zou, Chang, Cai, Peiliang, Zheng, Shikang, Shi, Zhengyi, Feng, Liang 等 9 位作者

原文摘要

Diffusion Transformers (DiTs) have achieved state-of-the-art image and video generation performance, but sampling remains expensive due to repeated transformer forward passes over many timesteps. Feature caching offers a training-free way to accelerate inference by reusing or forecasting hidden representations, yet recent forecasting-based methods derive their coefficients from hand-crafted formulas (e.g., Taylor expansion), which ultimately reduce to fixed linear combinations of a few historical features. Such fixed coefficients are suboptimal and fragile under aggressive skipping. In this paper, we first show that existing forecasting-based caching methods can be unified in a common linear form, and then analyze DiT feature trajectories, finding that for most denoising steps the current feature can be reconstructed from past features with projection fidelity above 0.95, indicating that accurate linear prediction is feasible. Motivated by this, we propose L^2P (Learnable Linear Predictor), a simple data-driven caching framework that replaces hand-designed coefficients with learnable per-timestep weights trained on a small set of cached trajectories using a mean-squared error loss, converging in about 20 seconds on a single GPU. Extensive experiments on state-of-the-art DiTs demonstrate that L^2P consistently outperforms existing caching baselines: on FLUX.1-dev, L^2P achieves a 4.55xFLOPs reduction and 4.15xlatency speedup with a PSNR of 31.459, and on Qwen-Image and Qwen-Image-Lightning, it maintains high visual fidelity even under up to 7.18xacceleration, where prior methods suffer from noticeable quality degradation. These results show that learning linear predictors is a practical and effective alternative to designing increasingly complex forecasting formulas for efficient diffusion model inference. The implementation code is available at https://github.com/Aredstone/L2P-Cache.

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:be091207b70c0960962c6e85589f933bCVF · CVPR 2026

ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Data

Ye, Yaoqin, Xu, Yiteng, Sun, Qin, Zhu, Xinge, Sun, Yujing, Ma, Yuexin

原文摘要

Human behaviors in real-world environments are inherently interactive, with an individual's motion shaped by surrounding agents and the scene. Such capabilities are essential for applications in virtual avatars, interactive animation, and human-robot collaboration. We target real-time human interaction-to-reaction generation, which generates the ego's future motion from dynamic multi-source cues, including others' actions, scene geometry, and optional high-level semantic inputs. This task is fundamentally challenging due to (i) limited and fragmented interaction data distributed across heterogeneous single-person, human-human, and human-scene domains, and (ii) the need to produce low-latency yet high-fidelity motion responses during continuous online interaction. To address these challenges, we propose ReMoGen (Reaction Motion Generation), a modular learning framework for real-time interaction-to-reaction generation. ReMoGen leverages a universal motion prior learned from large-scale single-person motion datasets and adapts it to target interaction domains through independently trained Meta-Interaction modules, enabling robust generalization under data-scarce and heterogeneous supervision. To support responsive online interaction, ReMoGen performs segment-level generation together with a lightweight Frame-wise Segment Refinement module that incorporates newly observed cues at the frame level, improving both responsiveness and temporal coherence without expensive full-sequence inference. Extensive experiments across human-human, human-scene, and mixed-modality interaction settings show that ReMoGen produces high-quality, coherent, and responsive reactions, while generalizing effectively across diverse interaction scenarios.

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:be21203224b72eefeee48d0aa0ef6ca6CVF · CVPR 2026

LookasideVLN: Direction-Aware Aerial Vision-and-Language Navigation

Ning, Yuwei, Zhao, Ganlong, Qin, Yipeng, Liu, Si, Liu, Yang, Lin, Liang, Li, Guanbin

原文摘要

Aerial Vision-and-Language Navigation (Aerial VLN) enables unmanned aerial vehicles (UAVs) to follow natural language instructions and navigate complex urban environments.While recent advances have achieved progress through large-scale memory graphs and lookahead path planning, they remain limited by shallow instruction understanding and high computational cost. In particular, existing methods rely primarily on landmark descriptions, overlooking directional cues--a key source of spatial context in human navigation.In this work, we propose LookasideVLN, a new paradigm that exploits directional cues in natural language to achieve both more accurate spatial reasoning and greater computational efficiency. LookasideVLN comprises three core components: (1) an Egocentric Lookaside Graph (ELG) that dynamically encodes instruction-relevant landmarks and their directional relationships, (2) a Spatial Landmark Knowledge Base (SLKB) that provides lightweight memory retrieval from prior navigation experiences, and (3) a Lookaside MLLM Navigation Agent that aligns multimodal information from user instructions, visual observations, and landmark-direction information from ELG for path planning.Extensive experiments show that LookasideVLN significantly outperforms the state-of-the-art CityNavAgent, even with a single-level lookahead, demonstrating that leveraging directional cues is a powerful yet efficient strategy for Aerial VLN.

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:be3487fd8eac8217a6a99a4227302b28CVF · CVPR 2026

AeroAgent: A Vision-Physics-Decision Framework for Aerodynamic Vehicle Design

Liu, Ye, Liu, Shouyi, Yang, Huiyu, Gu, Jianghang, Fan, Wenhao, Yang, Zhongxin, Wang, Ding, Chen, Simeng 等 12 位作者

原文摘要

Modern generative models can propose striking 3D vehicle shapes from text and images, but turning these sketches into aerodynamically efficient, regulation compliant designs still requires weeks of high-fidelity computational fluid dynamics (CFD) and manual iteration. As a result, fast 3D generation without trustworthy physics in the loop does little to reduce end-to-end design time. We study how an AI agent can close this loop under a strict CFD budget. We introduce AeroAgent, a vision-physics-decision framework built around a single 3D, editable surface representation for vehicle shapes. A vision module turns text and 2D references into diverse, standardized 3D candidates and supports image-level edits. A physics module, AeroFormer is a geometry-guided Transformer surrogate trained on a large-scale vehicle aerodynamics dataset of roughly 50k CFD simulations; three task-specific heads predict drag Cd, surface pressure, and velocity fields on shared 3D grids. A decision module encodes regulatory size limits and aesthetic constraints as feasibility tests, uses prototype priors and surrogate sensitivities to guide free-form deformation edits, and runs a budget-aware propose evaluate refine loop in which only the final top K shapes are confirmed by high-fidelity CFD. In extensive experiments across five common vehicle classes, running only five propose evaluate refine iterations per vehicle reduces drag by an average of 2-12% and cuts high-fidelity CFD calls by 50-80% compared to baseline workflows, while preserving or improving styling quality.

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:be569ab4e8e21ab2ed67aeb0b9c353baCVF · CVPR 2026

Towards High-resolution and Disentangled Reference-based Sketch Colorization

Yan, Dingkun, Wang, Xinrui, Wang, Ru, Li, Zhuoru, Yu, Jinze, Iwasawa, Yusuke, Matsuo, Yutaka, Guo, Jiaxian

原文摘要

Sketch colorization models have been widely studied to automate and assist in the creation of animation frames and digital illustrations. However, current methods are still not satisfactory for industrial standard applications in high-resolution synthesis and precise controllability of details. To further enhance the synthesis quality and controllability, we propose an image-referenced sketch colorization method based on the powerful SDXL backbone and leverage sketches as spatial guidance and RGB images as color references. A split cross-attention mechanism is coupled with spatial masks to separately colorize the foreground and background regions to avoid spatial entanglement. A tagger network trained on a massive anime-style image dataset is employed to extract attribution-level information from reference images and integrated into the pipeline to provide precise control signals for synthesis. However, the increased resolution and number of attention layers in the SDXL backbone and precise reference information from the tagger network cause severe entanglement during colorization. We consequently combine a foreground encoder and a background encoder for disentanglement and better synthesis quality. Furthermore, a high-quality annotated and paired sketch colorization dataset is collected for fine-tuning. The proposed method is the first to achieve high resolution high quality sketch colorization with precise control, and obvious outperforms existing methods in quantitative and qualitative validations, as well as user studies in both quality and controllability. Ablation study reveals the influence of each component. Code and dataset will be made publicly available upon paper acceptance.

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:be63a928c9f0220b41a709a0364fcf11CVF · CVPR 2026

Harnessing the Power of Foundation Models for Accurate Material Classification

Lin, Qingran, Yang, Fengwei, Zhu, Chaolun

原文摘要

Material classification has emerged as a critical task in computer vision and graphics, supporting the assignment of accurate material properties to a wide range of digital and real-world applications. While traditionally framed as an image classification task, this domain faces significant challenges due to the scarcity of annotated data, limiting the accuracy and generalizability of trained models. Recent advances in vision-language foundation models (VLMs) offer promising avenues to address these issues, yet existing solutions leveraging these models still exhibit unsatisfying results in material recognition tasks. In this work, we propose a novel framework that effectively harnesses foundation models to overcome data limitations and enhance classification accuracy. Our method integrates two key innovations:(a) a robust image generation and auto-labeling pipeline that creates a diverse and high-quality training dataset with material-centric images, and automatically assigns labels by fusing object semantics and material attributes in text prompts; (b) a prior incorporation strategy to distill information from VLMs, combined with a joint fine-tuning method that optimizes a pre-trained vision foundation model alongside VLM-derived priors, preserving broad generalizability while adapting to material-specific features.Extensive experiments demonstrate significant improvements on multiple datasets. We show that our synthetic dataset effectively captures the characteristics of real world materials, and the integration of priors from vision-language models significantly enhances the final performance.

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:be6c744323dc9a5a427c50c94bbfaed8CVF · CVPR 2026

GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics

Jin, Modi, Zhang, Yiming, Sun, Boyuan, Zhang, Dingwen, Cheng, Ming-Ming, Hou, Qibin

原文摘要

This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions. Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies, which conflict with geographic characteristics. To address these issues, we first introduce GeoSeek, a new geolocation dataset comprising CoT data annotated by geographic experts and professional players. We further thoroughly explore the inherent characteristics of geographic tasks and propose a geo-similarity reward and a consistency reward assessed by a consistency agent to assist training. This encourages the model to converge towards correct answers from a geographic perspective while ensuring the integrity and consistency of its reasoning process. Experimental results show that GeoAgent outperforms existing methods and a series of general VLLMs across multiple grains, while generating reasoning that closely aligns with humans. Pretrained model and data will be openly available.

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:be7576d09384a19b964f052627822d5aCVF · CVPR 2026

Compositional Transformation Reasoning for Composed Video Retrieval

Huang, Sihong, Wu, Jiaxin, Jiang, Dongmei, Cai, Yi, Wang, Yaowei, Wei, Xiaoyong

原文摘要

Composed Video Retrieval aims to retrieve a target video given a reference video and a textual modification describing the desired change. The core challenge lies in modeling compositional multimodal transformations, i.e., how entities, actions, and scenes evolve across video and language modalities in response to fine-grained textual edits. Existing methods address this issue by training on large-scale video-text-video triplets or by generating dense textual descriptions to capture subtle visual differences. However, these supervised approaches often rely on noisy web-scale data and dataset-specific correspondences, leading to overfitting and limited generalization in diverse or fine-grained scenarios, while also failing to effectively model compositional and temporal transformations. We propose Multi-objective Reasoning (MoRe), a zero-shot framework based on MLLMs for multi-objective candidate selection and fine-grained transformation reasoning. Our method decomposes the compositional transformation into three complementary reasoning dimensions, i.e., entity, action, and scene, and performs pairwise candidate reasoning to explicitly capture semantic evolution over time. Furthermore, we introduce a recall-oriented multi-objective candidate selection module that identifies high-quality retrieval targets by jointly balancing visual, textual, and multimodal similarities before transformation reasoning. Experiments on EgoCVR and WebVid-CoVR demonstrate the effectiveness of our method over state-of-the-art approaches under the zero-shot setting, with R@1 improvements of +5.8 and +10.8, respectively.

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:be946a547dd2cc397314936023bf5e9fCVF · CVPR 2026

Memory Matters: Boosting Training-Free Zero-Shot Temporal Action Localization with a Learnable Lookup Table

Jiang, Han, Tang, Haoyu, Mu, Xiaoxuan, Li, Chen, Zhu, Jihua

原文摘要

Zero-Shot Temporal Action Localization (ZS-TAL) aims to classify and localize actions in untrimmed videos that are unseen during training. Existing training-based ZS-TAL methods typically rely on fine-tuning models on large-scale annotated training data. This can be impractical in real-world applications and damage its generalization. As a result, Training-Free ZS-TAL has gained attention, which directly leveraging Vision-Language Models (VLM) enables action localization without any additional training. However, current techniques perform test-time adaptation independently on each video, neglecting the potential benefit of accumulating knowledge from historical test videos. To address this, we propose a learnable lookup table (LLT) framework. During testing, we continuously update lookup table by incorporating high-confidence, diverse lookup candidates to construct action-positive lookup item. Additionally, we introduce a learnable residual module to adapt the corresponding lookup item to the current video context features. Finally, we employ refined activation scores to select accurate video frames and further adjust the text prototypes. This simple yet effective text-visual collaboration enables training-free ZS-TAL to harness historical videos. Extensive experiments show our method significantly outperforms state-of-the-art zero-shot VLM baselines, validating the effectiveness of our framework.

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:beb4ffaa5f0e4e526b31176fad981730CVF · CVPR 2026

DiffBMP: Differentiable Rendering with Bitmap Primitives

Hong, Seongmin, Kim, Junghun James, Kim, Daehyeop, Chung, Insoo, Chun, Se Young

原文摘要

We introduce **DiffBMP**, a scalable and efficient differentiable rendering engine for a collection of bitmap images. Our work addresses a limitation that traditional differentiable renderers are constrained to vector graphics, given that most images in the world are bitmaps. Our core contribution is a highly parallelized rendering pipeline, featuring a custom CUDA implementation for calculating gradients. This system can, for example, optimize the position, rotation, scale, color, and opacity of thousands of bitmap primitives all in under 1 min using a consumer GPU. We employ and validate several techniques to facilitate the optimization: soft rasterization via Gaussian blur, structure-aware initialization, noisy canvas, and specialized losses/heuristics for videos or spatially constrained images. We demonstrate DiffBMP is not just an isolated tool, but a practical one designed to integrate into creative workflows. It supports exporting compositions to a native, layered file format, and the entire framework is publicly accessible via an easy-to-hack Python package.

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: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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Cycle-Consistent Tuning for Layered Image Decomposition

Gu, Zheng, Lu, Min, Sun, Zhida, Lischinski, Dani, Cohen-Or, Daniel, Huang, Hui

原文摘要

Disentangling visual layers in real-world images is a persistent challenge in vision and graphics, as such layers often involve non-linear and globally coupled interactions, including shading, reflection, and perspective distortion. In this work, we present an in-context image decomposition framework that leverages large diffusion foundation models for layered separation. We focus on the challenging case of logo-object decomposition, where the goal is to disentangle a logo from the surface on which it appears while faithfully preserving both layers. Our method fine-tunes a pretrained diffusion model via lightweight LoRA adaptation and introduces a cycle-consistent tuning strategy that jointly trains decomposition and composition models, enforcing reconstruction consistency between decomposed and recomposed images. This bidirectional supervision substantially enhances robustness in cases where the layers exhibit complex interactions. Furthermore, we introduce a progressive self-improving process, which iteratively augments the training set with high-quality model-generated examples to refine performance. Extensive experiments demonstrate that our approach achieves accurate and coherent decompositions and also generalizes effectively across other decomposition types, suggesting its potential as a unified framework for layered image decomposition.

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

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High-Precision Dichotomous Image Segmentation via Depth Integrity-Prior and Fine-Grained Patch Strategy

Liu, Xianjie, Fu, Keren, Zhao, Qijun

原文摘要

High-precision dichotomous image segmentation (DIS) is a task of extracting fine-grained objects from high-resolution images.Existing methods trade efficiency for accuracy: non-diffusion methods are fast but suffer from weak semantics and unstable spatial priors, causing false detections; diffusion-based methods offer high accuracy via strong generative priors but are computationally expensive.In depth maps, a complete object appears as a low variance region with a smooth interior and sharp boundaries, whereas the background exhibits a chaotic, high variance pattern due to disconnected surfaces at varying depths. We refer to this as the depth integrity-prior.Inspired by this, and noting that DIS currently lacks depth maps, we leverage pseudo-depth information from monocular depth estimation models to obtain essential semantic understanding, thereby rapidly revealing spatial differences across target objects and the background.To exploit this prior, we propose the Prior-guided Depth Fusion Network (PDFNet), which fuses RGB and pseudo-depth features for depth-aware structure perception. We further introduce a novel depth integrity-prior loss to enforce depth consistency in segmentation and a fine-grained enhancement module with adaptive patch selection to sharpen boundaries.Notably, PDFNet with DAM-v2 achieves SOTA (F^ max _b 0.915 on DIS-VD and 0.915 on DIS-TE) using less than half the params of diffusion-based methods.

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

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Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation

Karimijafarbigloo, Sanaz, Khosravi, Armin, Kheyrkhah, Alireza, Azad, Reza, Reyes, Mauricio, Merhof, Dorit

原文摘要

Multi-rater medical image segmentation captures the inherent ambiguity of clinical interpretation, where diagnostic boundaries vary across experts and imaging devices. Existing approaches often reduce this diversity to consensus labels or treat rater differences as noise, resulting in overconfident and poorly calibrated models. We propose a harmonized probabilistic framework that disentangles acquisition artifacts from genuine annotator variability through adaptive feature conditioning and frequency-domain personalization. A lightweight Harmonizer Network implicitly models scanner-specific artifacts and performs dynamic feature modulation to standardize latent representations, ensuring that uncertainty reflects anatomy rather than noise. To represent rater-specific styles, we introduce High-Frequency Prompt Modules that operate in the spectral domain to encode annotator-dependent boundary precision and textural sensitivity. These prompts adaptively modulate harmonized features to produce personalized yet anatomically consistent segmentations. Furthermore, a Generalized Energy Distance (GED)-based regularization aligns the generative distribution with empirical annotation variability, promoting diversity where experts disagree and consensus where they converge. Experiments on LIDC-IDRI and NPC-170 show SOTA aggregated and individualized segmentation, with notable GED reductions and improved Dice scores, especially on noisy cases. Beyond accuracy, the model exhibits clinically meaningful uncertainty, confidence rises in agreement regions and declines in ambiguous areas, supporting its use as a reliable and interpretable tool for multi-expert clinical workflows.

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

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VSRELL: A Simple Baseline for Video Super-Resolution and Enhancement in Low-Light Environment

Hui, Yanming, Shang, Fanhua, Liu, Hongying, Wang, Ben, Zhang, Zhenwei, Wan, Liang, Feng, Wei, Xue, Tong 等 9 位作者

原文摘要

We propose an integrated learning scheme of Video Super-Resolution and Enhancement in Low-Light environment, named VSRELL, which aims to recover Well-Illuminated High-Resolution (WIHR) sequence from Low-Light Low-Resolution (LLLR) counterparts. Due to the complex coupling of multiple degradations, this joint task has received relatively little attention. Our approach jointly models illumination enhancement and spatial-temporal super-resolution to disentangle intertwined degradations. Specifically, we introduce an Illumination-Noise Co-Optimization (INCO) network that employs a dynamic window partitioning strategy to explicitly model physical priors of illumination variations and noise distributions within individual frames of a long-term sequence. This effectively suppresses cross-frame noise accumulation and illumination flickering, achieving simultaneous optimization of motion compensation and brightness correction.Additionally, an Illumination-Sensitive Feature Propagation (ISFP) mechanism is introduced, which utilizes hierarchical illumination-sensing gating unit to adaptively modulate feature channel responses. By adjusting feature propagation intensity and using memory feature attenuation strategy, it can enhance the weighting of high-quality features and suppress error accumulation propagation and strengthen transmission efficiency. The experiments show that VSRELL can explicitly strengthen the brightness continuity and texture fidelity of the restored output, maintaining temporal consistency across the video.

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

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Object-Generalized Re-Identification: A Step Towards Universal Instance Perception

Chen, Shuoyi, Wu, Yurui, Ye, Mang

原文摘要

The object re-identification (ReID) task aims to recognize the same individual object across diverse viewpoints and sensing conditions.Although person and vehicle ReID have achieved remarkable success, most existing methods are built on the assumption that training and testing data come from the same object category.This constraint requires separate models for each category, which limits scalability and generalization.To address this limitation, we introduce Object-Generalized Re-Identification (OG-ReID), a new paradigm that learns unified identity representations transferable across different object categories.Unlike conventional domain generalization that focuses on appearance variations within a single category, OG-ReID deals with category shifts caused by intrinsic structural differences in identity cues. To achieve this goal, we introduce the Meta-Generalized Object Re-Identification (MGOR) framework, which treats meta-learning as semantic distributional regularization, exposing the model to controlled category shifts so that invariance emerges as an equilibrium between semantic diversity and identity discrimination.Extensive evaluations on more than 100 unseen object categories from multiple domains show that MGOR outperforms existing ReID approaches without any target-domain adaptation, advancing toward universal identity perception beyond domain and category boundaries.

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

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Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles

Liu, Jiawei, Gong, Xun, Fang, Fen, Yang, Muli, Qu, Bohao, Hu, Yunfeng, Chen, Hong, Yang, Xulei 等 9 位作者

原文摘要

Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an intuitive interface, yet translating passenger open-ended instructions into control signals--without sacrificing interpretability and traceability--remains a challenge. This study proposes an instruction-realization framework that leverages a large language model (LLM) to interpret instructions, generates executable scripts that schedule multiple model predictive control (MPC)-based motion planners based on real-time feedback, and converts planned trajectories into control signals. This scheduling-centric design decouples semantic reasoning from vehicle control at different timescales, establishing a transparent, traceable decision-making chain from high-level instructions to low-level actions. Due to the absence of high-fidelity evaluation tools, this study introduces a benchmark for open-ended instruction realization in a closed-loop setting. Comprehensive experiments reveal that the framework significantly improves task-completion rates over instruction-realization baselines, reduces LLM query costs, achieves safety and compliance on par with specialized AD approaches, and exhibits considerable tolerance to LLM inference latency. For more qualitative illustrations and a clearer understanding, please refer to Supplementary Material.

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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VideoRealBench: A Chain-of-Thought Realism Evaluation Benchmark for Generated Human-Centric Videos

Yang, Min, Zhang, Xinwen, Tang, Jialei, Zhou, Xin, Li, Kehan, Huang, Zeyi, Wang, Limin

原文摘要

With the great advancement of video generation models, a growing number of content creators and researchers are leveraging these technologies to produce large volumes of human-centric videos for content creation and customized data generation for specific tasks. Although existing video generation models are capable of producing videos with high visual quality, their inadequate understanding of video realism results in generating unrealistic videos. While various evaluators have emerged to assess the quality of generated videos, they are trained from low-quality generated videos and data annotations, leading to misaligned ratings with human preferences. They also lack interpretability due to the absence of chain-of-thought reasoning. To address these issues, we propose VideoRealBench, a comprehensive benchmark for evaluating the realism of generated human-centric videos. We leverage a rating scale designed from human preferences to score videos and provide three-step rationales, thereby creating a finely-annotated dataset VideoRealDataset and proposing an evaluator VideoRealEval capable of providing reliable scores along with detailed rationales. VideoRealEval achieves a Pearson's linear correlation coefficient (PLCC) of 57.07% and a Spearman's rank correlation coefficient (SROCC) of 56.78% on VideoRealDataset, demonstrating closer alignment with human preferences than existing evaluators. Our code and dataset are available at https://github.com/MCG-NJU/VideoRealBench.

cs.CV

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

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RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution

Mosleh, Ali, Ali, Faraz, Zhang, Fengjia, Tsogkas, Stavros, Lee, Junyong, Brown, Michael S., Levinshtein, Alex

原文摘要

Digital zoom on smartphones relies on learning-based super-resolution (SR) models that operate on RAW sensor images, but obtaining sensor-specific training data is challenging due to the lack of ground-truth images. Synthetic data generation via "unprocessing" pipelines offers a potential solution by simulating the degradations that transform high-resolution (HR) images into their low-resolution (LR) counterparts. However, these pipelines can introduce domain gaps due to incomplete or unrealistic degradation modeling. In this paper, we demonstrate that principled and carefully designed degradation modeling can enhance SR performance in real-world conditions. Instead of relying on generic priors for camera blur and noise, we model device-specific degradations through calibration and unprocess publicly available rendered images into the RAW domain of different smartphones. Using these image pairs, we train a single-image SR model that operates on mosaicked RAW images and evaluate it on real data from a held-out device. Our experiments show that accurate degradation modeling leads to noticeable improvements, with our SR model outperforming baselines trained on large pools of arbitrarily chosen degradations.

cs.CV

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

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RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation

Zhou, Shijie, Zhu, Bin, Yang, Jiarui, Zhao, Xiangyu, Chen, Jingjing, Jiang, Yu-Gang

原文摘要

Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow(RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the probability density function. We further present LIBERO-Anomaly-10, a benchmark comprising three categories of robotic anomalies for simulation evaluation. RC-NF achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks. Real-world experiments demonstrate that RC-NF operates as a plug-and-play module for VLA models (e.g., Pi0) , providing a real-time OOD signal that enables state-level rollback or task-level replanning when necessary, with a response latency under 100 ms. These results have demonstrated that our RC-NF noticeably enhances the robustness and adaptability of VLA-based robotic systems in dynamic environments.

cs.CV

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

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Parameter-Efficient Adaptation for MLLMs via Implicit Modality Decomposition

Zhang, Mingfang, Wang, Yunhong, Wang, Lu, Chen, Jiaxin

原文摘要

Parameter-efficient fine-tuning (PEFT) has become a compelling approach for adapting large language models (LLMs) into multimodal large language models (MLLMs), enabling them to handle diverse modalities with substantially lower memory and computational costs. However, most existing PEFT methods neglect the issue of modality-imbalanced learning, which is characterized by the excessive dominance of text modality in updating parameters, thus incurring insufficient learning of non-text modalities and leading to performance degradation. To address this issue, we propose a novel parameter-efficient adaptation method for MLLMs, namely Implicit Modality Decomposition (IMoD), based on LoRA. It firstly decomposes the learnable parameters into the non-overlapped text-specific, non-text-specific and modality-sharing components, thereby alleviating modality imbalance. To further guide the optimization of these components toward specific modalities, we propose Modality-Specific Decoupling Constraint that suppresses cross-modal interference among modality-specific parameters, and Modality-Agnostic Alignment Constraint that encourages modality-sharing component to capture well-aligned, modality-invariant semantics. Extensive experiments across diverse multimodal settings and LLM architectures demonstrate that our method consistently delivers significant performance gains, particularly achieving an averaged 3.3% improvements on the audio-visual-text tasks without sacrificing the parameter and inference efficiency. Code is available at https://github.com/mmffzzz/IMoD.git.

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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Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion

Pan, Yueming, Feng, Ruoyu, Dai, Qi, Wang, Yuqi, Lin, Wenfeng, Guo, Mingyu, Luo, Chong, Zheng, Nanning

原文摘要

Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. This indicates the preceding semantics potentially benefit the texture generation by providing a semantic anchor. Recent advances have integrated semantic priors from pretrained visual encoders to further enhance LDMs, yet they still denoise semantic and VAE-encoded texture synchronously, neglecting such ordering. Observing these, we propose Semantic-First Diffusion (SFD), a latent diffusion paradigm that explicitly prioritizes semantic formation. SFD first constructs composite latents by combining the compact semantic latent, which is extracted from pretrained visual encoder via a dedicated Semantic VAE, with the texture latent. The core of SFD is to denoise the semantic and texture latents asynchronously using separate noise schedules: semantics precede textures by a temporal offset, providing clearer high-level guidance for texture refinement and enabling natural coarse-to-fine generation. On ImageNet 256x256 with guidance, SFD achieves FID 1.06 (LightningDiT-XL) and FID 1.04 (1.0B LightningDiT-XXL), while achieving up to 100xfaster convergence than original DiT without guidance. SFD also improves existing methods like ReDi and VA-VAE, demonstrating the effectiveness of asynchronous, semantics-led modeling.

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

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Neural Differentiation in Deep Networks: A Theoretical Framework for Expressivity and Representational Diversity

Wang, Boyuan, Jiang, Richard

原文摘要

We begin by developing a mathematical framework of neural differentiation, formulated at the level of individual neurons. This framework formalizes the principle that each neuron should acquire a distinct representational role within the network, thereby avoiding redundancy and maximizing collective expressivity. Differentiation is quantified through the Neural Differentiation Index (NDI), a loss-aware measure that characterizes neuron significance from geometric, informational, and curvature-based perspectives within a unified framework. The NDI enables a rigorous characterization of how strongly a neuron diverges from its peers in both function and importance, and supports theoretical guarantees: we establish formal bounds on the error increase under NDI-guided elimination, thereby providing provable safety margins for network compression. Building on this foundation, we introduce Neural Differentiation Pruning (NDP) as a practical instantiation. NDP leverages NDI to perform adaptive, training-time neuron sparsification, followed by targeted fine-tuning, guiding networks toward compact yet highly differentiated backbones. Although the terminology draws loose intuition from biological differentiation, the framework is fully mathematical and architecture-agnostic. Experiments on modern vision benchmarks and architectures show that NDP achieves substantial structured sparsity while maintaining--or even improving--accuracy and robustness, underscoring the practical impact of the differentiation framework.

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

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E-3DPSM: A State Machine for Event-based Egocentric 3D Human Pose Estimation

Deshmukh, Mayur, Akada, Hiroyasu, Rhodin, Helge, Theobalt, Christian, Golyanik, Vladislav

原文摘要

Event cameras offer multiple advantages in monocular egocentric 3D human pose estimation from head-mounted devices, such as millisecond temporal resolution, high dynamic range, and negligible motion blur. Existing methods effectively leverage these properties, but suffer from low 3D estimation accuracy, insufficient in many applications (e.g., immersive VR/AR). This is due to the design not being fully tailored towards event streams (e.g., their asynchronous and continuous nature), leading to high sensitivity to self-occlusions and temporal jitter in the estimates. This paper rethinks the setting and introduces E-3DPSM, an event-driven continuous pose state machine for event-based egocentric 3D human pose estimation. E-3DPSM aligns continuous human motion with fine-grained event dynamics; it evolves latent states and predicts continuous changes in 3D joint positions associated with observed events, which are fused with direct 3D human pose predictions, leading to stable and drift-free final 3D pose reconstructions. E-3DPSM runs in real-time at 80 Hz on a single workstation and sets a new state of the art in experiments on two benchmarks, improving accuracy by up to 19% (MPJPE) and temporal stability by up to 2.7x.

cs.CV

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

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