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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3e423ad943c1db553feacc3f1cecb7bdCVF · CVPR 2026

GeoRelight: Learning Joint Geometrical Relighting and Reconstruction with Flexible Multi-Modal Diffusion Transformers

Xue, Yuxuan, Liang, Ruofan, Zakharov, Egor, Bagautdinov, Timur, Cao, Chen, Nam, Giljoo, Saito, Shunsuke, Pons-Moll, Gerard 等 9 位作者

原文摘要

Relighting a person from a single photo is an attractive but ill-posed task, as a 2D image ambiguously entangles 3D geometry, intrinsic appearance, and illumination. Current methods either use sequential pipelines that suffer from error accumulation, or they do not explicitly leverage 3D geometry during relighting, which limits physical consistency. Since relighting and estimation of 3D geometry are mutually beneficial tasks, we propose a unified Multi-Modal Diffusion Transformer (DiT) that jointly solves for both: GeoRelight. We make this possible through two key technical contributions: isotropic NDC-Orthographic Depth (iNOD), a distortion-free 3D representation compatible with latent diffusion models; and a strategic mixed-data training method that combines synthetic and auto-labeled real data. By solving geometry and relighting jointly, GeoRelight achieves state-of-the-art results in photorealistic relighting with physically-consistent shadows, as well as high-fidelity 3D reconstruction and intrinsic estimation from a single image. Project page: https://yuxuan-xue.com/georelight

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:3e463e46f6cecf4ef92e5dd33c549db8CVF · CVPR 2026

BarbieGait: An Identity-Consistent Synthetic Human Dataset with Versatile Cloth-Changing for Gait Recognition

Cai, Qingyuan, Hou, Saihui, Hu, Xuecai, Huang, Yongzhen

原文摘要

Gait recognition, as a reliable biometric technology, has seen rapid development in recent years while it faces significant challenges caused by diverse clothing styles in the real world. This paper introduces BarbieGait, a synthetic gait dataset where real-world subjects are uniquely mapped into a virtual engine to simulate extensive clothing changes while preserving their gait identity information. As a pioneering work, BarbieGait provides a controllable gait data generation method, enabling the production of large datasets to validate cross-clothing issues that are difficult to verify with real-world data. However, the diversity of clothing increases intra-class variance and makes one of the biggest challenges to learning cloth-invariant features under varying clothing conditions. Therefore, we propose GaitCLIF (Gait-oriented CLoth-Invariant Feature) as a robust baseline model for cross-clothing gait recognition. Through extensive experiments, we validate that our method significantly improves cross-clothing performance on BarbieGait and the existing popular gait benchmarks. We believe that BarbieGait, with its extensive cross-clothing gait data, will further advance the capabilities of gait recognition in cross-clothing scenarios and promote progress in related research. The source code and dataset are available at https://github.com/BarbieGait/BarbieGait.

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:3e72c9db72fef4c10a4255e04ac3b67cCVF · CVPR 2026

STCDiT: Spatio-Temporally Consistent Diffusion Transformer for High-Quality Video Super-Resolution

Chen, Junyang, Dong, Jiangxin, Sun, Long, Yang, Yixin, Pan, Jinshan

原文摘要

We present STCDiT, a video super-resolution framework built upon a pre-trained video diffusion model, aiming to restore structurally faithful and temporally stable videos from degraded inputs, even under complex camera motions. The main challenges lie in maintaining temporal stability during reconstruction and preserving structural fidelity during generation. To address these challenges, we first develop a motion-aware VAE reconstruction method that performs segment-wise reconstruction, with each segment clip exhibiting uniform motion characteristic, thereby effectively handling videos with complex camera motions. Moreover, we observe that the first-frame latent extracted by the VAE encoder in each clip, termed the anchor-frame latent, remains unaffected by temporal compression and retains richer spatial structural information than subsequent frame latents. We further develop an anchor-frame guidance approach that leverages structural information from anchor frames to constrain the generation process and improve structural fidelity of video features. Coupling these two designs enables the video diffusion model to achieve high-quality video super-resolution. Extensive experiments show that STCDiT outperforms state-of-the-art methods in terms of structural fidelity and temporal consistency.

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:3e85abdeb4aae60920951f0a77415e14CVF · CVPR 2026

DynamicTree: Interactive Real Tree Animation via Sparse Voxel Spectrum

Li, Yaokun, Ding, Lihe, Chen, Xiao, Tan, Guang, Xue, Tianfan

原文摘要

Generating dynamic and interactive 3D trees has wide applications in virtual reality, games, and world simulation. However, existing methods still face various challenges in generating structurally consistent and realistic 4D motion for complex real trees. In this paper, we propose DynamicTree, the first framework that can generate long-term, interactive 3D motion for 3DGS reconstructions of real trees. Unlike prior optimization-based methods, our approach generates dynamics in a fast feed-forward manner. The key success of our approach is the use of a compact sparse voxel spectrum to represent the tree movement. Given a 3D tree from Gaussian Splatting reconstruction, our pipeline first generates mesh motion using the sparse voxel spectrum and then binds Gaussians to deform the mesh. Additionally, the proposed sparse voxel spectrum can also serve as a basis for fast modal analysis under external forces, allowing real-time interactive responses. To train our model, we also introduce 4DTree, the first large-scale synthetic 4D tree dataset containing about 8.5k animated tree meshes with semantic labels and 100-frame motion sequences. Extensive experiments demonstrate that our method achieves realistic and responsive tree animations, significantly outperforming existing approaches in both visual quality and computational efficiency.

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:3e86d5bea5e9d04317f8540a9563de6fCVF · CVPR 2026

Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection

VCR, Sairam, Lalla, Rishabh, Dayal, Aveen, Kulkarni, Tejal, Lalla, Anuj, Balasubramanian, Vineeth N., Khan, Muhammad Haris

原文摘要

Current state-of-the-art approaches in Source-Free Object Detection (SFOD) typically rely on Mean-Teacher self-labeling. However, domain shift often reduces the detector's ability to maintain strong object-focused representations, causing high-confidence activations over background clutter and unreliable pseudo-labels from the detection head. While prior works mainly refine the pseudo-labels, they overlook the underlying need to strengthen the feature space itself. We propose FALCON-SFOD (Foundation-Aligned Learning with Clutter Suppression and Noise Robustness), a framework designed to enhance object-focused adaptation under domain shift. It consists of two complementary components. SPAR (\underline S patial \underline P rior-\underline A ware \underline R egularization) leverages the generalization strength of vision foundation models to regularize the detector's feature space. Using class-agnostic binary masks derived from OV-SAM, SPAR promotes structured and foreground-focused activations by guiding the network toward object regions. IRPL (\underline I mbalance-aware Noise \underline R obust \underline P seudo-\underline L abeling) complements SPAR by promoting balanced and noise-tolerant learning under severe foreground-background imbalance. Guided by a theoretical analysis that connects these designs to tighter localization and classification error bounds, FALCON-SFOD achieves competitive performance across SFOD benchmarks. Our code is available in https://github.com/Sairam13001/FALCON-SFOD.

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:3ea20539187e230f04f2804a4fefc80aCVF · CVPR 2026

CoV-Align: Efficient Fine-grained Cross-Modal Alignment with Cohesive Visual Semantics Priority

Liu, Hengqi, Zhou, Wanting, Kong, Longteng, Feng, Fangxiang, Ren, Lei, Chen, Wei, Wang, Xiaojie

原文摘要

Cross-modal alignment aims to learn semantically consistent latent representations across diverse modalities. Prevailing methods rely on a text-guided aggregation paradigm to achieve fine-grained alignment, while they suffer from redundant patch-word correlations and high computational costs. To address these issues, we propose CoV-Align, an effective and efficient fine-grained cross-modal alignment framework with cohesive visual semantics priority. Through a semantically convergent attention mechanism, it progressively aggregates meaningful visual patches in a text-free manner. We design a coarse visual semantic feature extractor that integrates deformable attention and consistent assign attention to group patches with semantic consistency. A cohesive and discriminative feature optimization is presented to enhance intra-semantic cohesion and inter-semantic discriminability of visual region features, resulting in explicit improvements in cross-modal alignment. Extensive experiments demonstrate that CoV-Align achieves state-of-the-art performance on the Flickr30K and MS-COCO benchmarks. Notably, it delivers a 3-5xcomputational speedup compared to pioneer approaches, offering compelling advantages for large-scale multi-modal tasks.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3ea338eb5da59c7edcc7148bd8ae4e38CVF · CVPR 2026

HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning

Lee, Eunju, Kim, MiHyeon, Kwon, JuneHyoung, Lee, Yoonji, Kim, JiHyun, Jang, Soojin, Kim, YoungBin

原文摘要

Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods assume homogeneous domains and balanced data distributions, limiting real-world applicability where data arises from heterogeneous disciplines with imbalanced sample availability and varying visual complexity. We identify Domain Gravity, a representational asymmetry where data imbalance across heterogeneous domains causes overrepresented or low-entropy domains to disproportionately influence the embedding space, leading to prototype drift and degraded performance on underrepresented or high-entropy domains. To address this, we introduce Cross-Discipline Variable Few-Shot Class-Incremental Learning (XD-VSCIL), a benchmark capturing real-world heterogeneity and imbalance where Domain Gravity naturally intensifies. We propose Hybrid Prototype Calibration (HyCal), a training-free method combining cosine similarity and Mahalanobis distance to capture complementary geometric properties--directional alignment and covariance-aware magnitude--yielding stable prototypes under imbalanced heterogeneous conditions. Operating on frozen CLIP embeddings, HyCal achieves consistent retention-adaptation improvements while maintaining efficiency. Experiments show HyCal effectively mitigates Domain Gravity and outperforms existing methods in imbalanced cross-domain incremental learning.

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:3ea9cadf24dc2620a50f75c8be054586CVF · CVPR 2026

Geometric-Aware Hypergraph Reasoning for Novel Class Discovery in Point Cloud Segmentation

Zhang, Zihao, Wu, Aming, Li, Yang, Han, Yahong, Shen, Jialie

原文摘要

Novel Class Discovery in Point Cloud Segmentation is recently proposed, aiming to leverage knowledge from known classes to automatically segment unlabeled classes within point clouds. The core of this task lies in leveraging the geometric and semantic knowledge of multiple known classes to achieve semantic understanding and segmentation of novel classes.However, existing methods overlook the high-order associations between known and novel classes, relying solely on binary associations for class assignment and novel class reasoning, which leads to less precise semantic segmentation.To address these issues, we introduce a hypergraph structure to model high-order associations among classes, enabling collaborative reasoning from known classes to novel classes, extending beyond traditional binary relations.Additionally, existing methods focus excessively on extracting semantic information when processing point cloud data, neglecting the importance of geometric features. To address this, we introduce Geometric-Aware Prototypes, enhancing the model's ability to capture geometric spatial information.By propagating geometric information through hyperedges, our method enhances the understanding of spatial distributions across classes, improving segmentation accuracy.Significant performance improvements achieved on the SemanticKITTI and SemanticPOSS datasets demonstrate the superiority of our method.

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:3eb1b8da603d0c7776b2f1c84f7e5481CVF · CVPR 2026

Gloria: Consistent Character Video Generation via Content Anchors

Yang, Yuhang, Zhang, Fan, Pi, Huaijin, Zeng, Ailing, Guo, Shuai, Xu, Guowei, Zhai, Wei, Cao, Yang 等 9 位作者

原文摘要

Digital characters are central to modern media, yet generating character videos with long-duration, consistent multi-view appearance and expressive identity remains challenging. Existing approaches either provide insufficient context to preserve identity or leverage non-character-centric information as the "memory", leading to suboptimal consistency.Recognizing that character video generation inherently resembles an "outside-looking-in" scenario. In this work, we propose represent the character's visual attributes through a compact set of anchor frames.This design provides stable references for consistency, while reference-based video generation inherently faces challenges of copy-pasting and multi-reference conflicts. To address these, we introduce two mechanisms: Superset Content Anchoring, providing intra- and extra-training clip cues to prevent duplication, and RoPE as Weak Condition, encoding positional offsets to distinguish multiple anchors.Furthermore, we construct a scalable pipeline to extract these anchors from massive videos. Experiments show our method generates high-quality character videos exceeding 10 minutes, and achieves expressive identity and appearance consistency across views, surpassing existing 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:3ec51f9b81c80c69eaa5fbc5f66418a6CVF · CVPR 2026

CaTok: Taming Mean Flows for One-Dimensional Causal Image Tokenization

Chen, Yitong, Wu, Zuxuan, Qiu, Xipeng, Jiang, Yu-Gang

原文摘要

Autoregressive (AR) language models rely on causal tokenization, but extending this paradigm to vision remains non-trivial. Current visual tokenizers either flatten 2D patches into non-causal sequences or enforce heuristic orderings that misalign with the "next-token prediction" pattern. Recent diffusion autoencoders similarly fall short: conditioning the decoder on all tokens lacks causality, while applying nested dropout mechanism introduces imbalance. To address these challenges, we present CaTok, a 1D causal image tokenizer with a MeanFlow decoder. By selecting tokens over time intervals and binding them to the MeanFlow objective, CaTok learns causal 1D representations that support both fast one-step generation and high-fidelity multi-step sampling, while naturally capturing diverse visual concepts across token intervals. To further stabilize and accelerate training, we propose a straightforward regularization REPA-A, which aligns encoder features with Vision Foundation Models (VFMs). Experiments demonstrate that CaTok achieves state-of-the-art results on ImageNet reconstruction, reaching 0.75 FID, 22.53 PSNR and 0.674 SSIM with fewer training epochs, and the AR model attains performance comparable to leading approaches. Project website is available in https://sharelab-sii.github.io/catok-web.

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:3ec5b803707cd07406166ad0bca4d354CVF · CVPR 2026

Globscope: Toward a Global View of the Loss Landscape

Mustaq, Mashiat, Tricoche, Xavier M.

原文摘要

Understanding the global structure of neural network loss landscapes is important for gaining insight into model merging, hyperparameter selection, generalization, and the relationships between distinct solutions. Visualizing the global structure of loss landscapes is very challenging because of the high dimensionality of the parameter space of neural networks. Prior work has primarily focused on visualizing the loss landscape around one single basin, missing how different minima or solutions relate to each other. We introduce Globscope, a framework that provides a global view of the loss landscape across multiple solutions or basins. Globscope learns a low-dimensional non-linear manifold of model parameters using an autoencoder, enabling both latent-space visualization and reconstruction of full model weights. Then it summarizes the relations and connections among minima on this manifold through topological data analysis. Our framework produces continuous, interpretable visualizations that reveal global connectivity patterns in the landscape. We compare Globscope with kernel-based methods and demonstrate its superior performance in preserving the global structure across diverse solutions. We further show how Globscope can be used to analyze two applications: revealing global low-loss solution pathways between distinct solutions using mode connectivity algorithms, and visualizing permutation symmetries of different solutions using re-basin approaches.

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:3ecb6871208ad79e8c19e2655f6a457aCVF · CVPR 2026

VVS: Accelerating Speculative Decoding for Visual Autoregressive Generation via Partial Verification Skipping

Dong, Haotian, Li, Ye, Lu, Rongwei, Tang, Chen, Xia, Shu-Tao, Wang, Zhi

原文摘要

Visual autoregressive (AR) generation models have demonstrated strong potential for image generation, yet their next-token-prediction paradigm introduces considerable inference latency. Although speculative decoding (SD) has been proven effective for accelerating visual AR models, its "draft one step, then verify one step" paradigm prevents a direct reduction in the number of forward passes, limiting its acceleration potential. Motivated by the interchangeability of visual tokens, we explore verification skipping in the SD process for the first time to explicitly cut the number of target model forward passes, thereby reducing inference latency. By analyzing the characteristics of the drafting stage, we observe that verification redundancy and stale feature reusability are key factors to maintain generation quality while improving speed for verification-free steps. Inspired by these two observations, we propose a novel SD framework VVS to accelerate \underline \text v isual AR model via partial \underline \text v erification \underline \text s kipping, which integrates three complementary modules: (1) a verification-free token selector with dynamically truncation, (2) token level feature caching and reuse, and (3) fine-grained skipped step scheduling. Consequently, VVS reduces the number of target model forward passes by 2.8x relative to vanilla AR decoding while maintaining competitive generation quality, offering a superior speed-quality trade-off over conventional SD frameworks and revealing strong potential to reshape the SD paradigm. Our code is available at https://github.com/HyattDD/VVS.

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:3ee5db124c653ade18d850e11e0c1874CVF · CVPR 2026

DeX-Portrait: Disentangled and Expressive Portrait Animation via Explicit and Latent Motion Representations

Shi, Yuxiang, Li, Zhe, Wang, Yanwen, Zhu, Hao, Cao, Xun, Liu, Ligang

原文摘要

Portrait animation from a single source image and a driving video is a long-standing problem. Recent approaches tend to adopt diffusion-based image/video generation models for realistic and expressive animation. However, none of these diffusion models realizes high-fidelity disentangled control between the head pose and facial expression, hindering applications like expression-only or pose-only editing and animation. To address this, we propose DeX-Portrait, a novel approach capable of generating expressive portrait animation driven by disentangled pose and expression signals. Specifically, we represent the pose as an explicit global transformation and the expression as an implicit latent code. First, we design a powerful motion trainer to learn both pose and expression encoders for extracting precise and decomposed driving signals. Then we propose to inject the pose transformation into the diffusion model through a dual-branch conditioning mechanism, and the expression latent through cross attention. Finally, we design a progressive hybrid classifier-free guidance for more faithful identity consistency. Experiments show that our method outperforms state-of-the-art baselines on both animation quality and disentangled controllability.

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:3eec75769ec38650aace49f5233bf789CVF · CVPR 2026

CMR-RD: Long-Tailed Adaptive VLM for Explainable CMR Diagnosis

Li, Yansong, Qiu, Zhongxi, Tian, Yun, Jinyu, Zheng, Li, Shuo

原文摘要

Cardiac magnetic resonance (CMR) is the clinical gold standard for assessing cardiovascular diseases, but its interpretation relies on expert experience and remains challenging, particularly for identifying rare diseases. Existing automated methods lack interpretable reasoning processes, limiting clinical adoption. Although vision-language models (VLMs) possess basic visual understanding and text generation capabilities, they still lack verifiable reasoning chains in medical diagnosis and underperform on minority classes in long-tail distributions. To address these challenges, we propose CMR-RD, to our knowledge the first VLM for interpretable diagnosis in CMR, capable of generating explicit diagnostic chains aligned with imaging evidence. We construct a CMR dataset that reflects real-world clinical distributions, comprising five disease categories (including two rare conditions) plus normal controls. Building on this, the general-purpose VLM is aligned to medical and CMR semantics using large-scale medical vision-text data, and cold-start training is used to enhance its understanding of medical concepts and basic reasoning. To enhance reasoning and performance on rare samples, we propose Group Phase Policy Optimization (GPPO), which combines online multi-stage reinforcement learning (RL) with adaptive sampling. GPPO enables the model to proactively explore rare and underperforming classes, thereby effectively mitigating long-tail bias. Experiments demonstrate that CMR-RD achieves highly competitive accuracy and reasoning-chain correctness compared with medical and general VLMs, shows stronger recognition of rare categories.

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: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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3efe17033a1bfcbb49650b737a0bfe69CVF · CVPR 2026

UniLDiff: Unlocking the Power of Diffusion Priors for All-in-One Image Restoration

Cheng, Zihan, Zhou, Liangtai, Chen, Dian, Tang, Ni, Luo, Xiaotong, Xie, Yuan, Qu, Yanyun

原文摘要

All-in-One Image Restoration (AiOIR) has emerged as a promising yet challenging research direction. To address the core challenges of diverse degradation modeling and detail preservation, we propose UniLDiff, a unified framework enhanced with degradation- and detail-aware mechanisms, unlocking the power of diffusion priors for robust image restoration. Specifically, we introduce a Degradation-Aware Feature Fusion (DAFF) to dynamically inject low-quality features into each denoising step via decoupled fusion and adaptive modulation, enabling implicit modeling of diverse and compound degradations. Furthermore, we design a Detail-Aware Expert Module (DAEM) in the decoder to enhance texture and fine-structure recovery through expert routing. Extensive experiments across multi-task and mixed degradation settings demonstrate that our method consistently achieves state-of-the-art performance, highlighting the practical potential of diffusion priors for unified image restoration.

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:3f08ff27911a0b5cd5d9af2fc383ffedCVF · CVPR 2026

NESTOR: A Nested MOE-based Neural Operator for Large-Scale PDE Pre-Training

Sun, Dengdi, Zhou, Xiaoya, Wang, Xiao, Si, Hao, Lyu, Wanli, Tang, Jin, Luo, Bin

原文摘要

Neural operators have emerged as an efficient paradigm for solving PDEs, overcoming the limitations of traditional numerical methods and significantly improving computational efficiency. However, due to the diversity and complexity of PDE systems, existing neural operators typically rely on a single network architecture, which limits their capacity to fully capture heterogeneous features and complex system dependencies. This constraint poses a bottleneck for large-scale PDE pre-training based on neural operators. To address these challenges, we propose a large-scale PDE pre-trained neural operator based on a nested Mixture-of-Experts (MoE) framework. In particular, the image-level MoE is designed to capture global dependencies, while the token-level Sub-MoE focuses on local dependencies. Our model can selectively activate the most suitable expert networks for a given input, thereby enhancing generalization and transferability. We conduct large-scale pre-training on twelve PDE datasets from diverse sources and successfully transfer the model to downstream tasks. Extensive experiments demonstrate the effectiveness of our approach. The code is publicly available at: https://github.com/Event-AHU/OpenFusion/tree/main/NESTOR.

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:3f277721b7ec6ec524343de4f0d4cc64CVF · CVPR 2026

EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions

Yoon, Taegyoon, Han, Yegyu, Ji, Seojin, Park, Jaewoo, Kim, Sojeong, Kwon, Taein, Kim, Hyung-Sin

原文摘要

Smart glass is emerging as an useful device since it provides plenty of insights under hands-busy, eyes-on-task situations. To understand the context of the wearer, 6D object pose estimation in egocentric view is becoming essential. However, existing 6D object pose estimation benchmarks fail to capture the challenges of real-world egocentric applications, which are often dominated by severe motion blur, dynamic illumination, and visual obstructions. This discrepancy creates a significant gap between controlled lab data and chaotic real-world application. To bridge this gap, we introduce EgoXtreme, a new large-scale 6D pose estimation dataset captured entirely from an egocentric perspective. EgoXtreme features three challenging scenarios--industrial maintenance, sports, and emergency rescue--designed to introduce severe perceptual ambiguities through extreme lighting, heavy motion blur, and smoke. Evaluations of state-of-the-art generalizable pose estimators on EgoXtreme indicate that their generalization fails to hold in extreme conditions, especially under low light. We further demonstrate that simply applying image restoration (e.g., deblurring) offers no positive improvement for extreme conditions. While performance gain has appeared in tracking-based approach, implying using temporal information in fast-motion scenarios is meaningful. We conclude that EgoXtreme is an essential resource for developing and evaluating the next generation of pose estimation models robust enough for real-world egocentric vision. The dataset and code are available at https://taegyoun88.github.io/EgoXtreme/

cs.CV

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

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RegionFuse: Region-Adaptive Pixel Distribution Learning for Infrared and Visible Image Fusion

Xia, Jianghan, Song, Hong, Li, Jinfu, Lin, Yucong, Ma, Shihan, Fan, Jingfan, Ai, Danni, Fu, Tianyu 等 10 位作者

原文摘要

Infrared and Visible Image Fusion (IVIF) aims to combine complementary information from infrared and visible images to overcome the limitations of a single modality. While existing methods typically employ fixed or sample-adaptive fusion paradigms where fusion weights are static or derived from global pixel distributions, they often overlook spatial inconsistencies in pixel distribution within images, leading to suboptimal performance. To address this issue, we propose RegionFuse, a Region-Adaptive Pixel Distribution Learning Network for IVIF, which dynamically generates fusion weights based on local pixel distributions to construct a region-wise adaptive fusion paradigm. RegionFuse introduces a Mixture of Region Attention (MoRA) mechanism, which assigns each region to several specialized experts, enabling region-level feature interaction tailored to specific local distributions. Furthermore, we design a Region Feature Compression Module (RFCM) and place it after each MoRA to enhance informative regions and suppress redundant ones. Extensive experiments on various benchmarks demonstrate the superiority and robustness of RegionFuse, especially in handling non-uniform pixel distributions. Evaluations on NIR-VIS and downstream tasks further confirm its generalizability and practical utility. The code is available at https://github.com/DarkIceField/RegionFuse.

cs.CV

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

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Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures

Liu, Zeyao, Zhao, Zhendong, Chen, Xiaojun, Zhao, Xin, Xuan, Yuexin, Ji, Xiaoshuang

原文摘要

Existing ViT backdoor attacks based on backbone-overwriting full-tuning are computationally expensive and inflict performance degradation. This has forced adversaries towards the Visual Parameter-Efficient Fine-Tuning (PEFT) paradigm, dominated by adapter-based (e.g., LoRA) and prompt-based (e.g., VPT) approaches. While adapter security has seen initial study, the risks of the burgeoning prompt-based ecosystem remain critically unexplored. We fill this critical gap, exposing how the evolution of VPT towards dynamic and context-aware architectures can facilitate a far more dangerous and emergent threat.This vulnerability arises even though these dynamic modules unlock superior benign performance. We propose VIPER, an attack framework built on a lightweight, dynamic Visual Prompt Generator (VPG) that demonstrates this vulnerability. Critically, this dynamic architecture enables Functional Fusion: an emergent phenomenon where malicious logic and benign task utility are tightly fused into the same sparse, high-magnitude parameter core. This fusion creates a formidable "hostage" dilemma, as pruning the attack necessarily destroys the benign performance. Comprehensive evaluations show VIPER effectively addresses the attacker's trilemma: VIPER not only achieves state-of-the-art performance on clean data, but also maintains near-100% ASR even under 90% VPG-module pruning (where LoRA attacks collapse), while adding only an imperceptible 0.06ms (1.16%) of inference latency. VIPER's results, driven by Functional Fusion, expose a new, paradigm-level risk in dynamic prompt architectures.

cs.CV

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

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MOMO: Mars Orbital MOdel Foundation Model for Mars Orbital Applications

Purohit, Mirali, Gajera, Bimal, Mehta, Irish, Tokas, Bhanu, Adler, Jacob, Lu, Steven, Dickenshied, Scott, Diniega, Serina 等 11 位作者

原文摘要

We introduce MOMO, the first multi-sensor foundation model for Mars remote sensing. MOMO uses model merge to integrate representations learned independently from three key Martian sensors (HiRISE, CTX, and THEMIS), spanning resolutions from 0.25 m/pixel to 100 m/pixel. Central to our method is our novel Equal Validation Loss (EVL) strategy, which aligns checkpoints across sensors based on validation loss similarity before fusion via task arithmetic. This ensures models are merged at compatible convergence stages, leading to improved stability and generalization. We train MOMO on a large-scale, high-quality corpus of 12 million samples curated from Mars orbital data and evaluate it on 9 downstream tasks from Mars-Bench. MOMO achieves better overall performance compared to ImageNet pre-trained, earth observation foundation model, sensor-specific pre-training, and fully-supervised baselines. Particularly on segmentation tasks, MOMO shows consistent and significant performance improvement. Our results demonstrate that model merging through an optimal checkpoint selection strategy provides an effective approach for building foundation models for multi-resolution data. The model weights, pretraining code, pretraining data, and evaluation code are available at: github.com/kerner-lab/MOMO.

cs.CV

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

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Multi-Modal Image Fusion via Intervention-Stable Feature Learning

Wang, Xue, Guan, Zheng, Qian, Wenhua, Wang, Chengchao, Ma, Runzhuo

原文摘要

Multi-modal image fusion integrates complementary information from different modalities into a unified representation. Current methods predominantly optimize statistical correlations between modalities, often capturing dataset-induced spurious associations that degrade under distribution shifts. In this paper, we propose an intervention-based framework inspired by causal principles to identify robust cross-modal dependencies. Drawing insights from Pearl's causal hierarchy, we design three principled intervention strategies to probe different aspects of modal relationships: i) complementary masking with spatially disjoint perturbations tests whether modalities can genuinely compensate for each other's missing information, ii) random masking of identical regions identifies feature subsets that remain informative under partial observability, and iii) modality dropout evaluates the irreplaceable contribution of each modality. Based on these interventions, we introduce a Causal Feature Integrator (CFI) that learns to identify and prioritize intervention-stable features maintaining importance across different perturbation patterns through adaptive invariance gating, thereby capturing robust modal dependencies rather than spurious correlations. Extensive experiments demonstrate that our method achieves SOTA performance on both public benchmarks and downstream high-level vision tasks. The Code can be available.

cs.CV

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

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Unlearning without Forgetting: Securely Removing Targeted Concepts from Large-Scale Vision-Language Open-Vocabulary Detectors

Wu, Zhongze, Su, Xiu, Yang, Feng, Niu, Dan, You, Shan, Luo, Yueyi, Long, Jun

原文摘要

Open-vocabulary detectors (OvOD) inherit tightly coupled cross-modal knowledge from web-scale pretraining, creating privacy, copyright, and compliance risks. Existing machine unlearning methods face geometric entanglement interference in OvOD: forgetting updates inevitably distort preserved knowledge due to shared semantic factors in decomposable embeddings. We introduce SafeDetect, a geometrically constrained unlearning framework that constructs a null-space from preserved knowledge embeddings offline, then constrains parameter updates to this orthogonal complement, mathematically preventing interference with retained concepts. Forgetting is achieved through a one-step mean-flow objective that drives forgotten concepts toward non-detectable, while multimodal decoupling prevents cross-modal recovery. We establish UOD-Bench, the first unified benchmark for OvOD unlearning, featuring 14.7K images with 67.3K region-phrase pairs across three tasks. Extensive experiments across UOD-Bench and standard benchmarks with diverse architectures (e.g., GroundingDINO, LLM-Det) demonstrate that SafeDetect achieves superior forgetting efficacy (64.75% improvement over NPO) while maintaining stable retention performance and significantly better zero-shot generalization, with 1.5x faster convergence than iterative methods.

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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Resolving the Identity Crisis in Text-to-Image Generation

Borse, Shubhankar, Farhadzadeh, Farzad, Hayat, Munawar, Porikli, Fatih

原文摘要

State-of-the-art text-to-image models suffer from a persistent identity crisis when generating scenes with multiple humans: producing duplicate faces, merging identities, and miscounting individuals. We present DisCo (Reinforcement with Diversity Constraints), a reinforcement learning framework that directly optimizes identity diversity both within images and across groups of generated samples. DisCo fine-tunes flow-matching models using Group-Relative Policy Optimization (GRPO), guided by a compositional reward that: (i) penalizes facial similarity within images, (ii) discourages identity repetition across samples, (iii) enforces accurate person counts, and (iv) preserves visual fidelity and prompt alignment via human preference scores. A single-stage curriculum stabilizes training as prompt complexity increases. Importantly, this method does not require any real data. On the DiverseHumans Testset, DisCo achieves 98.6% Unique Face Accuracy and near-perfect Global Identity Spread, outperforming open-source and proprietary models (e.g., Gemini, GPT-Image) while maintaining perceptual quality. Our results establish cross-sample diversity as a critical axis for resolving identity collapse, positioning DisCo as a scalable, annotation-free solution for multi-human image synthesis. Project page: https://qualcomm-ai-research.github.io/disco/.

cs.CV

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

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Towards Robust Multimodal Large Language Models Against Jailbreak Attacks

Yin, Ziyi, Cao, Yuanpu, Liu, Han, Wang, Ting, Chen, Jinghui, Ma, Fenglong

原文摘要

While multimodal large language models (MLLMs) have achieved remarkable success in recent advancements, their susceptibility to jailbreak attacks has come to light. In such attacks, adversaries exploit carefully crafted prompts to coerce models into generating harmful or undesirable content. Existing defense mechanisms often rely on external inference steps or safety alignment training, both of which are less effective and impractical when facing sophisticated adversarial perturbations in white-box scenarios. To address these challenges and bolster MLLM robustness, we introduce SAFEMLLM by adopting an adversarial training framework that alternates between an attack step for generating adversarial noise and a model updating step. At the attack step, SAFEMLLM generates adversarial perturbations through a newly proposed contrastive embedding attack (CoE-Attack), which optimizes token embeddings under a contrastive objective. SAFEMLLM then updates model parameters to neutralize the perturbation effects while preserving model utility on benign inputs. We evaluate SAFEMLLM across multiple MLLMs and six jailbreak methods spanning multiple modalities. Experimental results show that SAFEMLLM effectively defends against diverse attacks, maintaining robust performance and utilities.

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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Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flow

Hu, Shimin, Wei, Yuanyi, Zha, Fei, Guo, Yudong, Zhang, Juyong

原文摘要

Existing 3D editing methods rely on computationally intensive scene-by-scene iterative optimization and suffer from multi-view inconsistency. We propose an effective and feed-forward 3D editing framework based on the TRELLIS generative backbone, capable of modifying 3D models from a single editing view. Our framework addresses two key issues: adapting training-free 2D editing to structured 3D representations, and overcoming the bottleneck of appearance fidelity in compressed 3D features. To ensure geometric consistency, we introduce Voxel FlowEdit, an edit-driven flow in the sparse voxel latent space that achieves globally consistent 3D deformation in a single pass. To restore high-fidelity details, we develop a normal-guided single to multi-view generation module as an external appearance prior, successfully recovering high-frequency textures. Experiments demonstrate that our method enables fast, globally consistent, and high-fidelity 3D model editing.

cs.CV

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

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LNEM: Lunar Neural Elevation Model

Lee, Suwan, Yim, Jo Ryeong, Park, Kibaek, Kim, Dong-Gyu, Kim, Eunhyeuk, Jeong, Minsup, Sim, Chae Kyung, Lee, Seokju

原文摘要

High-resolution and high-precision digital elevation models (DEMs) of the lunar surface are essential for landing site selection and geological research. However, traditional stereo matching provides a limited representation of the 3D scene and struggling with non-textured regions and extreme illumination variations. Recent lunar neural rendering methods are also ill-suited for 3D reconstruction due to their reliance on simple pinhole approximations for pushbroom sensors. These challenges are further compounded by geometric misalignment, distributional bias, and labor-intensive handcrafted preprocessing in satellite image pipelines. To address these issues, we introduce the Lunar Neural Elevation Model (LNEM), a volumetric reconstruction method that explicitly incorporates the pushbroom imaging process. A core component of our approach is Lunar Studio, a multi-orbit dataset and pipeline constructed using Rigorous Sensor Models (RSMs) to produce geometrically consistent observations from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) and the Korea Pathfinder Lunar Orbiter (KPLO) Lunar Terrain Imager (LUTI). LNEM integrates this pushbroom camera formulation with learned shadow modeling, enabling geometrically grounded and illumination-aware volumetric rendering under challenging lunar lighting conditions. Extensive experiments demonstrate that LNEM achieves geometrically consistent reconstruction across multiple sensors under diverse viewing and illumination conditions, providing a scalable complement to conventional DEM pipelines. To support reproducibility and future lunar research, we release Lunar Studio, the multi-orbit dataset, and the LNEM reconstruction pipeline.

cs.CV

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

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PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving

Pan, Yining, Li, Shijie, Wu, Yuchen, Yang, Xulei, Zhao, Na

原文摘要

This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve generalization under domain shifts commonly encountered in real-world autonomous driving. A straightforward solution is to employ a pseudo-labeling strategy, which is widely used in UDA to generate supervision for unlabeled target data, combined with an mm-3DPS backbone. However, existing supervised mm-3DPS methods rely heavily on strong cross-modal complementarity between LiDAR and RGB inputs, making them fragile under domain shifts where one modality degrades (e.g., poor lighting or adverse weather). Moreover, conventional pseudo-labeling typically retains only high-confidence regions, leading to fragmented masks and incomplete object supervision, which are issues particularly detrimental to panoptic segmentation. To address these challenges, we propose PanDA, the first UDA framework specifically designed for multimodal 3D panoptic segmentation. To improve robustness against single-sensor degradation, we introduce an asymmetric multimodal augmentation that selectively drops regions to simulate domain shifts and improve robust representation learning. To enhance pseudo-label completeness and reliability, we further develop a dual-expert pseudo-label refinement module that extracts domain-invariant priors from both 2D and 3D modalities. Extensive experiments across diverse domain shifts, spanning time, weather, location, and sensor variations, significantly surpass state-of-the-art UDA baselines for 3D semantic segmentation.

cs.CV

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

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LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sight

Man, Yunze, Wang, Shihao, Zhang, Guowen, Bjorck, Johan, Gui, Liang-Yan, Fan, Jim, Kautz, Jan, Wang, Yu-Xiong 等 9 位作者

原文摘要

To act in the world, a model must name what it sees and know where it is in 3D. Today's vision-language models excel at open-ended 2D description and grounding, yet multi-object 3D detection remains largely missing from the VLM toolbox. We present LocateAnything3D, a VLM-native recipe that casts 3D detection as a next-token prediction problem. The key is a short, explicit Chain-of-Sight (CoS) sequence that mirrors how people reason from images: find an object in 2D, then infer its distance, size, and pose. The decoder first emits 2D detections as a visual chain-of-thought, then predicts 3D boxes under an easy-to-hard curriculum: across objects, a near-to-far order reduces early ambiguity and matches ego-centric utility; within each object, a center-from-camera, dimensions, and rotation factorization ranks information by stability and learnability. This VLM-native interface preserves open-vocabulary and visual-prompting capability without specialized heads. On the challenging Omni3D benchmark, our model achieves state-of-the-art results, with 49.89 AP, surpassing the previous best by +15.51 absolute improvement even when baseline is given ground-truth 2D boxes. It also generalizes zero-shot to held-out categories with strong calibration and robustness. By turning 3D detection into a disciplined next-token problem, LocateAnything3D offers a practical foundation for models to perceive in 3D.

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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Weight Space Representation Learning via Neural Field Adaptation

Yang, Zhuoqian, Salzmann, Mathieu, Süsstrunk, Sabine

原文摘要

In this work, we investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the optimization space through a pre-trained base model and low-rank adaptation (LoRA) can induce structure in weight space. Across reconstruction, generation, and analysis tasks on 2D and 3D data, we find that multiplicative LoRA weights achieve high representation quality while exhibiting distinctiveness and semantic structure. When used with latent diffusion models, multiplicative LoRA weights enable higher-quality generation than existing weight-space methods.

cs.CV

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

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