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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3f9f41a04db3ae575932d046821f6ff8CVF · CVPR 2026

Hermite Radial Basis Function for Surface Reconstruction via Differentiable Rendering

Blanc, Hugo, Deschaud, Jean-Emmanuel, Paljic, Alexis

原文摘要

Recent advances in novel view synthesis have enabled differentiable rendering methods to reconstruct 3D scenes directly from images. Algorithms such as 3D Gaussian Splatting and RayGauss use local basis functions to represent radiance fields, enabling fast, high-quality rendering of real-world scenes. However, these methods lack an exact geometric representation of the scene. In this work, inspired by Hermite Radial Basis Function (HRBF) implicits, we introduce a global implicit function constructed from local RBFs and their derivatives to represent surfaces. The proposed formulation enables learning scene geometry through differentiable rendering of an implicit function. By leveraging local basis functions, it achieves both an efficient geometric representation and fast rendering, using a bounding volume hierarchy (BVH) to accelerate intersections with the local basis functions. The implementation of our approach will be made publicly available upon the paper's publication.

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:3fab9797acc5b225c085597fd8dd46c4CVF · CVPR 2026

ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding

Cao, Shuo, Ma, Nan, Li, Jiayang, Li, Xiaohui, Shao, Lihao, Zhu, Kaiwen, Zhou, Yu, Pu, Yuandong 等 15 位作者

原文摘要

The rapid advancement of educational applications, artistic creation, and AI-generated content (AIGC) technologies has substantially increased practical requirements for comprehensive Image Aesthetics Assessment (IAA), particularly demanding methods capable of delivering both quantitative scoring and professional understanding. Multimodal Large Language Model (MLLM)-based IAA methods demonstrate stronger perceptual and generalization capabilities compared to traditional approaches, yet they suffer from modality bias (score-only or text-only) and lack fine-grained attribute decomposition, thereby failing to support further aesthetic assessment. In this paper, we present: (1) ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities; (2) ArtiMuse-10K, the first expert-curated IAA dataset comprising 10,000 images spanning 5 main categories and 15 subcategories, each annotated by professional experts with 8-dimensional attributes analysis and a holistic score. Both the model and dataset will be made public.

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:3fb3a9830933e1d178c04cf65c50f835CVF · CVPR 2026

LF-BVN: Blind-View Network for Self-Supervised Light Field Denoising

Guo, Longzhao, Zhang, Shuo, Gao, Chen, Tian, Qian, Lin, Youfang

原文摘要

Recent advances in learning-based Light Field (LF) image denoising have achieved impressive results. However, these methods rely heavily on large-scale noisy-clean image pairs and often fail to generalize to unseen or complex noise.In this work, we observe that the inherent multi-view consistency of LF images makes it highly unlikely for noise to be coherent across views, offering a more reliable supervisory signal for self-supervised denoising.Building on this insight, we extend the blind-spot principle to the LF domain and propose a novel LF Blind-View denoising Network (LF-BVN). We first introduce a geometric invariance mask that leverages angular redundancy for efficient full-view supervision. To enforce cross-view photometric consistency, we further introduce latent representation volumes and enforce consistency between them.Additionally, we exploit focus stacks to extract latent depth cues from noisy observations, providing further guidance.Extensive experiments show that LF-BVN achieves competitive denoising performance while maintaining strong cross-view consistency without requiring clean data or external supervision.

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:3fc36709ad2d976a39eefb6d258cfdbeCVF · CVPR 2026

RemedyGS: Defend 3D Gaussian Splatting Against Computation Cost Attacks

Li, Yanping, Liu, Zhening, Li, Zijian, Lin, Zehong, Zhang, Jun

原文摘要

As a mainstream technique for 3D reconstruction, 3D Gaussian splatting (3DGS) has been applied in a wide range of applications and services. Recent studies have revealed critical vulnerabilities in this pipeline and introduced computation cost attacks that lead to malicious resource occupancies and even denial-of-service (DoS) conditions, thereby hindering the reliable deployment of 3DGS. In this paper, we propose the first effective and comprehensive black-box defense framework, named RemedyGS, against such computation cost attacks, safeguarding 3DGS reconstruction systems and services. Our pipeline comprises two key components: a detector to identify the attacked input images with poisoned textures and a purifier to recover the benign images from their attacked counterparts, mitigating the adverse effects of these attacks. Moreover, we incorporate adversarial training into the purifier to enforce distributional alignment between the recovered and original natural images, thereby enhancing the defense efficacy. Experimental results demonstrate that our framework effectively defends against white-box, black-box, and adaptive attacks in 3DGS systems, achieving state-of-the-art performance in both safety and utility. Our code is available at https://github.com/Polly-LYP/RemedyGS.

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:3fc72f3386562e9c21b8c6d2acd8fe50CVF · CVPR 2026

PersonaLive! Expressive Portrait Image Animation for Live Streaming

Li, Zhiyuan, Pun, Chi-Man, Fang, Chen, Wang, Jue, Cun, Xiaodong

原文摘要

Current diffusion-based portrait animation models predominantly focus on enhancing visual quality and expression realism, while overlooking generation latency and real-time performance, which restricts their application range in the live streaming scenario. We propose PersonaLive, a novel diffusion-based framework towards streaming real-time portrait animation with multi-stage training recipes. Specifically, we first adopt hybrid implicit signals, namely implicit facial representations and 3D implicit keypoints, to achieve expressive image-level motion control. Then, a fewer-step appearance distillation strategy is proposed to eliminate appearance redundancy in the denoising process, greatly improving inference efficiency. Finally, we introduce an autoregressive micro-chunk streaming generation paradigm equipped with a sliding training strategy and a historical keyframe mechanism to enable low-latency and stable long-term video generation. Extensive experiments demonstrate that PersonaLive achieves state-of-the-art performance with up to **7-22**xspeedup over prior diffusion-based portrait animation models. The code will be publicly 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:4009b9fca7bed645a775392ca1093b29CVF · CVPR 2026

Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance

Yuan, Liangyu, Huang, Yufei, Lei, Mingkun, Zhao, Tong, Wang, Ruoyu, Chi, Changxi, Wang, Yiwei, Zhang, Chi

原文摘要

Diffusion models generate synthetic images through an iterative refinement process. However, the misalignment between the simulation-free objective and the iterative process often causes accumulated gradient error along the sampling trajectory, which leads to unsatisfactory results and a failure to generalize. Guidance techniques like Classifier Free Guidance (CFG) and AutoGuidance (AG) alleviate this by extrapolating between the main and inferior signal for stronger generalization. Despite empirical success, the effective operational regimes of prevalent guidance methods are still under-explored, leading to ambiguity when selecting the appropriate guidance method given a precondition. In this work, we first conduct synthetic comparisons to isolate and demonstrate the effective regime of guidance methods represented by CFG and AG from the perspective of weak-to-strong principle. Based on this, we propose a hybrid instantiation called SEG under the principle, taking the benefits of both. Furthermore, we demonstrate that the W2S principle along with SEG can be migrated into the training objective, improving the generalization ability of unguided diffusion models. We validate our approach with comprehensive experiments. At inference time, evaluations on SD3 and SD3.5 confirm that SEG outperforms existing training-free guidance variants. Training-time experiments on transformer architectures demonstrate the effective migration and performance gains in both conditional and unconditional settings. Code is available at https://github.com/Westlake-AGI-Lab/SGG

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:40288c941ce92512cf61a748dc059d46CVF · CVPR 2026

GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry

He, Xiankang, Lin, Peile, Cui, Ying, Guo, Dongyan, Shen, Chunhua, Zhang, Xiaoqin

原文摘要

Motion segmentation in dynamic scenes is highly challenging, as conventional methods heavily rely on estimating camera poses and point correspondences from inherently noisy motion cues. Existing statistical inference or iterative optimization techniques that struggle to mitigate the cumulative errors in multi-stage pipelines often lead to limited performance or high computational cost. In contrast, we propose a fully learning-based approach that directly infers moving objects from latent feature representations via attention mechanisms, thus enabling end-to-end feed-forward motion segmentation. Our key insight is to bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion. Supported by recent advances in 4D scene geometry reconstruction (e.g., \pi^3), the proposed method leverages reliable camera poses and rich spatial-temporal priors, which ensure stable training and robust inference for the model. Extensive experiments demonstrate that by eliminating complex pre-processing and iterative refinement, our approach achieves state-of-the-art motion segmentation performance with high efficiency. The code is available at:https://github.com/zjutcvg/GeoMotion.

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:403045e82bb4a22a59c26a48ac5f529dCVF · CVPR 2026

Block-Sparse Global Attention for Efficient Multi-View Geometry Transformers

Wang, Chung-Shien Brian, Schmidt, Christian, Piekenbrinck, Jens, Leibe, Bastian

原文摘要

Efficient and accurate feed-forward multi-view reconstruction has long been an important task in computer vision. Recent transformer-based models like VGGT, \pi^3 and MapAnything have demonstrated remarkable performance with relatively simple architectures. However, their scalability is fundamentally constrained by the quadratic complexity of global attention, which imposes a significant runtime bottleneck when processing large image sets. In this work, we empirically analyze the global attention matrix of these models and observe that the probability mass concentrates on a small subset of patch-patch interactions corresponding to cross-view geometric correspondences. Building on this insight and inspired by recent advances in large language models, we propose a training-free, block-sparse replacement for dense global attention, implemented with highly optimized kernels. Our method accelerates inference by more than 3xwhile maintaining comparable task performance. Evaluations on a comprehensive suite of multi-view benchmarks demonstrate that our approach seamlessly integrates into existing global attention-based architectures such as VGGT, \pi^3, and MapAnything, while substantially improving scalability to large image collections.

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:4047888bf89f65c836945743e705336fCVF · CVPR 2026

MARIS: Marine Open-Vocabulary Instance Segmentation

Li, Bingyu, Wang, Feiyu, Zhang, Da, Zhao, Zhiyuan, Gao, Junyu, Li, Xuelong

原文摘要

Most existing underwater instance segmentation approaches are constrained by close-vocabulary prediction, limiting their ability to recognize novel marine categories. To support evaluation, we introduce **MARIS** (_Marine Open-Vocabulary Instance Segmentation_), the first large-scale fine-grained benchmark for underwater Open-Vocabulary (OV) Instance segmentation (UOVIS), featuring a limited set of seen categories and diverse unseen categories. Although OV instance segmentation has shown promise on natural images, our analysis reveals that transfer to underwater scenes suffers from severe visual degradation (e.g., color attenuation) and semantic misalignment caused by lack underwater class definitions. To address these issues, we propose a unified framework with two complementary components. The Geometric Prior Enhancement Module (**GPEM**) leverages stable part-level and structural cues to maintain object consistency under degraded visual conditions. The Semantic Alignment Injection Mechanism (**SAIM**) enriches language embeddings with domain-specific priors, mitigating semantic ambiguity and improving recognition of unseen categories. Experiments show that our framework consistently outperforms existing OV baselines both In-Domain and Cross-Domain setting on MARIS, establishing a strong foundation for future underwater perception research. The code of this paper can be found in Github.

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:404d34aaece1cdafc5160b3655268b1dCVF · CVPR 2026

Assignment-Driven Hash Learning in a Hyper-Semantic Space for On-the-Fly Category Discovery

Yang, Kaibing, Wang, Yucheng, Luo, Tingzhang

原文摘要

On-the-fly Category Discovery (OCD) aims to dynamically identify both known and emerging unknown categories from streaming data, using supervision from only a limited set of labeled classes. Despite recent progress, our empirical analysis reveals fundamental limitations: existing methods suffer from cascading feature-to-hash degradation and severe space monopolization by known classes, fundamentally hindering novel category discovery. To address these coupled challenges, we introduce a principled two-stage framework.We first construct a Hyper-Semantic Space with dual geometric subspaces: a Derived Subspace employing parent-derived prototype augmentation to capture intra-class diversity and enhance inter-class discrimination, and a Calibrated Subspace synthesized through cross-prototype interpolation to impose distributional constraints and prevent representational collapse.Within this geometrically-constrained space, we perform Assignment-Driven Hash Learning, where Flexible Prototype Assignment (FPA) models intra-class variations and enhances inter-class separation, alongside Binary Hash Regularization (BHR) to enforce compact and discriminative hash representations. Our framework serves as a plug-and-play module, consistently improving state-of-the-art OCD methods across fine-grained benchmarks. Code will be released upon 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:404e74795f684ed3c3852d1b417073e2CVF · CVPR 2026

HiDRA: Hierarchical Degradation Representation and Adaptation with Generative Priors for Enhancing Infrared Vision

Chen, Zihang, Liu, Zhu, Yan, Changbo, Liu, Jinyuan, Liu, Risheng

原文摘要

Thermal infrared (TIR) imaging enables robust perception in adverse conditions. However, it often suffers from complex degradations (e.g., fixed-pattern noise and low-resolution) due to sensor limitations and environmental dynamics. Existing methods, whether traditional or learning-based, easily fail under composite and varying degradation. Pre-trained generative models showcase powerful capabilities for alleviating degradations but lack effective tools to adapt visible generative priors to TIR-specific characteristics. To overcome these challenges, we propose a Hierarchical Degradation Representation and Adaptation (HiDRA) framework to decompose the enhancement procedure into degradation representation estimation and generative model fine-tuning. The degradation representation estimation aims to disentangle TIR degradation patterns, which then guide the parameter adaptation for thermal image enhancement. Additionally, we introduce a hierarchical adaptation solution that aggregates learning across varying degradation levels, further improving robustness under various scenarios. Experiments across diverse types and degrees demonstrate the robustness of our approach and further validate its effectiveness on downstream tasks. Code is available at https://github.com/Zihang-Chen/HiDRA.

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:405aecd34ff582417202afdf0df40474CVF · CVPR 2026

ArtLLM: Generating Articulated Assets via 3D LLM

Wang, Penghao, Xie, Siyuan, Yan, Hongyu, Yang, Xianghui, Huang, Jingwei, Guo, Chunchao, Gu, Jiayuan

原文摘要

Creating interactive digital environments for gaming, robotics, and simulation relies on articulated 3D objects whose functionality emerges from their part geometry and kinematic structure. However, existing approaches remain fundamentally limited: optimization-based reconstruction methods require slow, per-object joint fitting and typically handle only simple, single-joint objects, while retrieval-based methods assemble parts from a fixed library, leading to repetitive geometry and poor generalization. To address these challenges, we introduce ArtLLM, a novel framework for generating high-quality articulated assets directly from complete 3D meshes. At its core is a 3D multimodal large language model trained on a large-scale articulation dataset curated from both existing articulation datasets and procedurally generated objects. Unlike prior work, ArtLLM autoregressively predicts a variable number of parts and joints, inferring their kinematic structure in a unified manner from the object's point cloud. This articulation-aware layout then conditions a 3D generative model to synthesize high-fidelity part geometries. Experiments on the PartNet-Mobility dataset show that ArtLLM significantly outperforms state-of-the-art methods in both part layout accuracy and joint prediction, while generalizing robustly to real-world objects. Finally, we demonstrate its utility in constructing digital twins, highlighting its potential for scalable robot 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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:40633ee5a244b8fa1423409506f6b0c1CVF · CVPR 2026

Machine Unlearning via Adaptive Gradient Reweighting and Multi-stage Objective Optimization

Lu, Juxin, Shi, Haoyu, Wang, Mengyao, Zhang, Huaiwen

原文摘要

Machine Unlearning (MU) focuses on removing the influence of training samples from pre-trained models without retraining the model entirely. Existing MU methods have made several efforts to enable complete forgetting while preserving the model's performance on remaining data. However, they typically apply equal weights across different data, overlooking the ambiguous decision boundaries between similar samples or approximate classes. This leads to unnecessary consumption of shallowly memorized samples and significant performance degradation for approximate retention classes. Additionally, the inherent inconsistency between forgetting and retention objectives results in gradient conflict and domination problems during training, hindering model convergence and degrading overall performance. To address these, we introduce a novel adaptive gradient reweighting that assigns importance weights to individual forget samples or vulnerable retention classes, thereby enabling more efficient unlearning and preserving the performance of approximate classes. Subsequently, we propose a multi-stage objective optimization strategy, which comprises three optimization stages: Direction Rectification, Temporal Stabilization, and Adaptive Objective Combination. This strategy rectifies the direction of conflicting gradients and prevents one task (forgetting or retention) from dominating the model update. Comprehensive analyses and extensive experiments on multiple public datasets demonstrate that our method achieves considerable performance improvements in various tasks and 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:406f3583e1062263c95d9016856451e8CVF · CVPR 2026

Thinking Beyond Labels: Vocabulary-Free Fine-Grained Recognition using Reasoning-Augmented LMMs

Demidov, Dmitry, Zaheer, Muhammad Zaigham, Han, Zongyan, Thawakar, Omkar, Anwer, Rao

原文摘要

Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Existing solutions for this problem remain limited by either the usage of a large and rigid list of vocabularies or by the dependency on complex pipelines with fragile heuristics where errors propagate across stages. Meanwhile, the ability of recent large multi-modal models (LMMs) equipped with explicit or implicit reasoning to comprehend visual-language data, decompose problems, retrieve latent knowledge, and self-correct suggests a more principled and effective alternative. Building on these capabilities, we propose FiNDR (Fine-grained Name Discovery via Reasoning), the first reasoning-augmented LMM-based framework for vocabulary-free fine-grained recognition. The system oper- ates in three automated steps: (i) a reasoning-enabled LMM generates descriptive candidate labels for each image; (ii) a vision-language model filters and ranks these candidates to form a coherent class set; and (iii) the verified names instantiate a lightweight multi-modal classifier used at inference time. Extensive experiments on popular fine-grained classification benchmarks demonstrate state-of-the-art per- formance under the vocabulary-free setting, with a significant relative margin of up to 18.8% over previous ap- proaches. Remarkably, the proposed method surpasses zero-shot baselines that exploit pre-defined ground-truth names, challenging the assumption that human-curated vo- cabularies define an upper bound. Ablations further confirm that advanced prompting techniques and built-in rea- soning mechanisms significantly enhance naming quality. Additionally, we show that carefully engineered prompts enable open-source LMMs to match proprietary counter- parts. These findings establish reasoning-augmented LMMs as an effective foundation for scalable, fully automated, open-world fine-grained visual recognition. The source code and relevant prompting guidelines will be released.

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:406f411ae2ebd0e617d3c62e654a1df9CVF · CVPR 2026

Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation

Wang, Xinshun, Li, Peiming, Wang, Ziyi, Fang, Zhongbin, Deng, Zhichao, Wu, Songtao, Li, Jason, Liu, Mengyuan

原文摘要

Human motion analysis tasks, such as temporal 3D pose estimation, motion prediction, and motion in-betweening, play an essential role in computer vision. However, current paradigms suffer from severe fragmentation. First, the field is split between "perception" models that understand motion from video but only output text, and "generation" models that cannot perceive from raw visual input. Second, generative MLLMs are often limited to single-frame, static poses using dense, parametric SMPL models, failing to handle temporal motion. Third, existing motion vocabularies are built from skeleton data alone, severing the link to the visual domain. To address these challenges, we introduce Superman, a unified framework that bridges visual perception with temporal, skeleton-based motion generation. Our solution is twofold. First, to overcome the modality disconnect, we propose a Vision-Guided Motion Tokenizer. Leveraging the natural geometric alignment between 3D skeletons and visual data, this module pioneers robust joint learning from both modalities, creating a unified, cross-modal motion vocabulary. Second, grounded in this motion language, a single, unified MLLM architecture is trained to handle all tasks. This module flexibly processes diverse, temporal inputs, unifying 3D skeleton pose estimation from video (perception) with skeleton-based motion prediction and in-betweening (generation). Extensive experiments on standard benchmarks, including Human3.6M, demonstrate that our unified method achieves state-of-the-art or competitive performance across all motion tasks. This showcases a more efficient and scalable path for generative motion analysis using skeletons.

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:407209ea7f40aaa6bbd04e53c9dfde02CVF · CVPR 2026

Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation

Wang, Shuo, Wang, Yucheng, Lian, Guoxin, Wang, Yongcai, Chen, Maiyue, Wang, Kaihui, Zhang, Bo, Su, Zhizhong 等 12 位作者

原文摘要

Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they have advanced within a multi-step instruction.However, recent Vision-Language-Action models focus on direct action prediction and earlier progress methods predict numeric achievements; both overlook the monotonic co-progression property of the observation and instruction sequences.Building on this insight, Progress-Think introduces semantic progress reasoning, predicting instruction-style progress from visual observations to enable more accurate navigation.To achieve this without annotations, we propose a three-stage framework.In the initial stage, Self-Aligned Progress Pretraining bootstraps a reasoning module via a novel differentiable alignment between visual history and instruction prefixes.Then, Progress-Guided Policy Pretraining injects learned progress states into the navigation context, guiding the policy toward consistent actions.Finally, Progress-Policy Co-Finetuning jointly optimizes both modules with tailored progress-aware reinforcement objectives.Experiments on R2R-CE and RxR-CE show substantial gains in success, efficiency, and interpretability, demonstrating semantic progress provides a more consistent and generalizable representation of navigation advancement.

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:4081b8fb25e362fff33f1e1f533748c2CVF · CVPR 2026

Mark4D: Temporally-Consistent Watermarking for 4D Gaussian Splatting

Lee, Jaejin, Jeong, Minjae, Park, Joonhyuk, Hwang, Yechan, Baek, Seunghun, Kim, Won Hwa

原文摘要

Embedding invisible and temporally consistent watermarks into dynamic 4D Gaussian Splatting (4DGS) models poses unique challenges due to continuous spatio-temporal deformation of Gaussians and diverse motion dynamics. Existing 3DGS watermarking methods, which directly fine-tune parameters within Gaussian splats, fail to preserve geometric fidelity and temporal coherence when extended to 4D settings. In this regime, we propose Mark4D, a temporally consistent watermarking technique that achieves robust, imperceptible, and motion-aware watermark embedding for 4DGS. Mark4D comprises 1) a decoder for watermark recovery in the latent video-text space for robustness against pixel-level distortions, 2) trajectory-aligned offsets that embed watermark signals along Gaussian motion paths to preserve geometry, and 3) a motion-adaptive loss weighting strategy that balances supervision across frames with varying motion intensities. Extensive experiments on synthetic and real-world dynamic scene datasets demonstrate the effectiveness of our method, establishing a foundation for secure and reliable protection of dynamic 4D scene assets.

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:40a537e0377cfaf97b8094976bbd12d5CVF · CVPR 2026

RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting

Shi, Ji, Ying, Xianghua, Xing, Bowei, Guo, Ruohao, Yue, Wenzhen

原文摘要

3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual quality. However, existing methods struggle with semi-transparent specular surfaces that exhibit both complex reflections and clear transmission, often producing blurry reflections or overly occluded transmission. To address this, we present RT-Splatting, a framework that disentangles each Gaussian's geometric occupancy from its optical opacity. This factorization yields a unified surface-volume scene representation with a single set of Gaussian primitives. Our hybrid renderer interprets this representation both as a surface to capture high-frequency reflections and as a volume to preserve clear transmission. To mitigate the ambiguity in jointly optimizing reflection and transmission, we introduce Specular-Aware Gradient Gating, which suppresses misleading gradients from highly specular regions into the transmission branch, effectively reducing distracting floaters. Experiments on challenging semi-transparent scenes show that RT-Splatting achieves state-of-the-art performance, delivering high-fidelity reflections and clear transmission with real-time rendering. Moreover, our factorization naturally enables flexible scene editing. The project page is available at https://sjj118.github.io/RT-Splatting.

cs.CV

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

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Learning to Refuse: Refusal-Aware Reinforcement Fine-Tuning for Hard-Irrelevant Queries in Video Temporal Grounding

Lee, Jin-Seop, Lee, SungJoon, Jung, SeongJun, Li, Boyang, Lee, Jee-Hyong

原文摘要

Video Temporal Grounding (VTG) aims to localize a temporal segment in a video corresponding to a natural language query. However, existing VTG models assume that a relevant segment always exists, causing them to always predict a target segment even when the query is irrelevant to the video. While recent approaches attempt to handle irrelevant queries, they can only reject those that are entirely unrelated to the video and still fail to handle hard-irrelevant queries that are semantically similar but not actually relevant.To address this, we propose Refusal-Aware Reinforcement Fine-Tuning (RA-RFT) to effectively refuse hard-irrelevant queries in VTG.Our method is based on the Group Relative Policy Optimization (GRPO) framework and integrates four reward objectives--format, refuse-IoU, explain, and query correction--to improve both relevance discrimination and fine-grained semantic reasoning.In addition, to effectively support RA-RFT, we construct a Hard-Irrelevant VTG (HI-VTG) dataset, which includes hard-irrelevant queries and their refusal answers.We demonstrate the effectiveness of our method across various relevance-aware VTG scenarios, including hard-irrelevant VTG, simply-shuffled RA-VTG, and human-annotated RA-VTG settings. We also show that the proposed method is scalable by applying it to various LVLM-based VTG models.

cs.CV

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

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Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation

Chharia, Aviral, De la Torre, Fernando

原文摘要

High-fidelity 3D Gaussian head avatar generation is critical for applications such as AR/VR, telepresence, and digital humans. Existing methods depend on multi-view datasets, 3D captures, or intermediate 2D view synthesis. In contrast, we learn both conditional and unconditional 3D head models from randomly sampled 2D images alone, without using multi-view data, 3D supervision, or intermediate view generation. We introduce MVCHead, a single-shot state space model that enforces multi-view consistency (MVC) directly in the 3D representation while regressing 3D Gaussians under these constraints. At its core, we propose a Hierarchical State Space (HiSS) block that progressively refines Gaussians from coarse to fine, while capturing long-range dependencies. Within each HiSS block, we modify Mamba's standard unidirectional scan with the proposed Hierarchical Bi-directional State Scan (HiBiSS) that aligns recurrence with the axes along which multi-view inconsistencies are strongest. Finally, we design an SE(3) Multi-view Critic that judges whether a set of self-renders arises from a single underlying 3D configuration, rewarding cross-view pixel alignment without observing real multi-view pairs. MVCHead achieves state-of-the-art perceptual quality, surpasses prior methods in both texture and geometric consistency, and maintains comparable shape consistency. To demonstrate scalability, we release FaceGS-10K, the first large-scale dataset of ready-to-use 3D Gaussian head assets for training and evaluation of 3D head models. Project Page and code: https://humansensinglab.github.io/MVCHead/

cs.CV

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

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Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing

He, Runze, Cheng, Yiji, Hang, Tiankai, Li, Zhimin, Xu, Yu, Yin, Zijin, Zhang, Shiyi, Dai, Wenxun 等 14 位作者

原文摘要

In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful execution of user intent. Although recent unified multimodal models exhibit promising understanding capabilities, these strengths often fail to transfer effectively to image generation. We introduce Re-Align, a unified framework that bridges the gap between understanding and generation through structured reasoning-guided alignment. At its core lies the In-Context Chain-of-Thought (IC-CoT), a structured reasoning paradigm that decouples semantic guidance and reference association, providing clear textual target and mitigating confusion among reference images. Furthermore, Re-Align introduces an effective RL training scheme that leverages a surrogate reward to measure the alignment between structured reasoning text and the generated image, thereby improving the model's overall performance on ICGE tasks. Extensive experiments verify that Re-Align outperforms competitive methods of comparable model scale and resources on both in-context image generation and editing tasks.

cs.CV

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

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Transition Matching Distillation for Fast Video Generation

Nie, Weili, Berner, Julius, Ma, Nanye, Liu, Chao, Xie, Saining, Vahdat, Arash

原文摘要

Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to their inefficient multi-step sampling process. In this work, we present Transition Matching Distillation (TMD), a novel framework for distilling video diffusion models into efficient few-step generators. The central idea of TMD is to match the multi-step denoising trajectory of a diffusion model with a few-step probability transition process, where each transition is modeled as a lightweight conditional flow. To enable efficient distillation, we decompose the original diffusion backbone into two components: (1) a main backbone, comprising the majority of early layers, that extracts semantic representations at each outer transition step; and (2) a flow head, consisting of the last few layers, that leverages these representations to perform multiple inner flow updates. Given a pretrained video flow model, we first introduce a flow head to the model, and adapt it into a conditional flow map. We then apply distribution matching distillation to the student model with flow head rollout in each transition step. Extensive experiments on distilling Wan2.1 1.3B and 14B text-to-video models demonstrate that TMD provides a flexible and strong trade-off between generation speed and visual quality. In particular, TMD outperforms existing distilled models under comparable inference costs in terms of visual fidelity and prompt adherence.

cs.CV

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

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Align Images Before You Generate

Zhang, Shihua, Shen, Qiuhong, Wang, Xinchao

原文摘要

Multi-image diffusion models can generate images like multi-views or videos to describe static or dynamic scenes, yet texture and structure drift persist, severely undermining the spatiotemporal consistency. Addressing this issue remains challenging, especially without any external geometric or semantic priors during the pure generative inference. In this paper, we introduce CorrAdapter, a plug-and-play adapter that discovers and exploits an innate property of the multi-image diffusion itself, aligning all output images before they are in fact generated. Specifically, CorrAdapter designs a bypass branch for transformer blocks in the multi-image diffusion model, encompassing a native correspondence constructor that builds reliable correspondences from the diffusion model's intermediate features, and an aligned area aggregator that integrates messages from only matching regions to avoid ambiguous information interactions. Given the native correspondences as guidance, CorrAdapter can enhance spatiotemporal consistency without any auxiliary inputs, and remains training-free and baseline-agnostic, which enables it to generalize seamlessly to various generation tasks. Additionally, we provide an optional training scheme to explore further-improved possibilities. Experiments on both static multi-view generation and dynamic video generation show that CorrAdapter consistently improves spatiotemporal consistency and perceptual quality over strong baselines, offering a simple yet versatile drop-in approach to geometrically faithful multi-image diffusion. Code is available at https://github.com/SuhZhang/CorrAdapter.

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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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VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction

Fan, Zhiwen, Zhang, Jian, Li, Renjie, Zhang, Junge, Chen, Runjin, Hu, Hezhen, Wang, Kevin, Wang, Peihao 等 19 位作者

原文摘要

The rapid advancement of Large Multimodal Models (LMMs) for 2D images and videos has sparked interest in extending these models to 3D scenes, with the goal of human-like visual-spatial intelligence. However, achieving deep spatial understanding comparable to human capabilities remains challenging for both model design and data acquisition. Existing methods often rely on external depth sensors for geometry capture or off-the-shelf algorithms for pre-constructing 3D maps, which limits their scalability.In this work, we introduce VLM-3R, a framework for Vision-Language Models that couples 3D reconstructive instruction tuning with scalable training data curation and a new benchmark for temporal reasoning. Specifically, VLM-3R processes monocular video frames with a geometry encoder that derives implicit 3D tokens representing scene context (spatial tokens) and camera motion (view tokens). In parallel, we build a scalable data creation pipeline with over 200K 3D reconstructive instruction-tuning question-answer pairs. To evaluate temporal reasoning, we further introduce the Vision-Spatial-Temporal Intelligence benchmark (VSTI-Bench), which contains over 138.6K question-answer pairs across five distinct tasks focused on evolving spatial relationships. Extensive experiments show that VLM-3R supports robust visual-spatial reasoning and improves the understanding of temporal 3D context changes, enabling monocular 3D spatial assistance and embodied reasoning.

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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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From Rays to Projections: Better Inputs for Feed-Forward View Synthesis

Wu, Zirui, Jiang, Zeren, Oswald, Martin R., Song, Jie

原文摘要

Feed-forward view synthesis models predict a novel view in a single pass with minimal 3D inductive bias. Existing works encode cameras as Plucker ray maps, which tie predictions to the arbitrary world coordinate gauge and make them sensitive to small camera transformations, thereby undermining geometric consistency. In this paper, we ask what inputs best condition a model for robust and consistent view synthesis. We propose projective conditioning, which replaces raw camera parameters with a target-view projective cue that provides a stable 2D input. This reframes the task from a brittle geometric regression problem in ray space to a well-conditioned target-view image-to-image translation problem. Additionally, we introduce a masked autoencoding pretraining strategy tailored to this cue, enabling the use of large-scale uncalibrated data for pretraining. Our method shows improved fidelity and stronger cross-view consistency compared to ray-conditioned baselines on our view-consistency benchmark. It also achieves state-of-the-art quality on standard novel view synthesis benchmarks.

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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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Mirror Illusion Art

Zhu, Xiaopei, Li, Zeyuan, Zhu, Jun, Hu, Xiaolin

原文摘要

Mirror Illusion Art is a novel reflection-conditioned 3D illusion where one object yields two target appearances (front and mirror). The task is formulated as inverse design from two target 2D images (front and mirror) to a printable 3D object with geometry and texture. Prior topology-driven and shadow-based approaches demand substantial manual effort, optimize shape only, and often yield non-smooth or incomplete geometry. To address these challenges, we propose AutoMIA, an automated Mirror Illusion Art design pipeline that jointly optimizes shape and color. To stabilize optimization and suppress artifacts, four mechanisms are introduced: (1) projection-alignment component (PAC) selection to reduce surface noise, (2) position-weighted adaptive (PWA) suppression for background noise, (3) internal voxel preservation (IVP) to prevent internal fractures, and (4) shape-color decoupled (SCD) optimization that balance shape and color optimization. AutoMIA generate diverse smooth Mirror Illusion artworks successfully both in the digital and physical world, with only around 76s design time and 2.6 GB memory on average using a single RTX 3090, advancing inverse graphics and computational design.

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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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PECCVAI: Overcoming the Brittleness of AI Image Watermarking Under Visual Paraphrasing Attacks

Dixit, Shreyas, Aziz, Ashhar, Bajpai, Shashwat, Sharma, Vasu, Chadha, Aman, Jain, Vinija, Das, Amitava

原文摘要

By 2026, up to 90% of online content may be synthetically generated, raising urgent concerns about the proliferation of AI-driven disinformation. In response, policymakers and technology companies are turning to watermarking as a safeguard: California's Bill AB 321 mandates watermarking of AI-generated media, while firms like Meta and Google have begun deploying watermarking systems to mitigate misuse. However, current watermarking methods remain brittle and vulnerable to attack. In this work, we introduce the visual paraphrase attack, a generative method that removes both visible and invisible watermarks from AI-generated images. The attack proceeds in two steps: (1) generating a descriptive caption from the original image, and (2) feeding this caption into a diffusion-based text-to-image model to produce a visually similar, watermark-free image. Our experiments show that this attack reliably removes watermarks while preserving semantic content of the original image, exposing a critical flaw in existing watermarking strategies. To counter this, we propose PECCAVI, the first watermarking technique explicitly designed to resist visual paraphrasing. PECCAVI embeds robust, high-fidelity watermarks (PSNR > 30 dB) in semantically stable image regions termed Non-Melting Points (NMPs) using multi-channel frequency domain encoding and noisy burnishing to obfuscate watermark locations and hinder reverse engineering. The method is model-agnostic and significantly more resilient than current alternatives. We also release the first benchmark dataset for visual paraphrasing attacks and open-source all code and resources, providing a foundation for future research on robust watermarking in the era of generative AI

cs.CV

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

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Streamlined Knowledge Distillation

Jeong, Hyeon-Jin, Lee, Han-Jin, Choi, Seok-Hwan

原文摘要

Logit-based Knowledge Distillation (KD) has emerged as a lightweight alternative to feature-based KD. Recent logit-based methods often rely on multi-knowledge alignment and relational modeling. These methods are often inefficient due to redundant objectives, suboptimal transformations, and poorly designed loss functions. Motivated by these issues, we propose Streamlined Knowledge Distillation (SKD), a simple yet effective logit-based method that transfers only two essential forms of knowledge without requiring additional alignment or relational modeling. Specifically, SKD transfers instance-wise knowledge via Kullback-Leibler divergence and direction-wise knowledge by aligning the Gramian matrix of normalized logits. For the latter, we introduce a Mahalanobis distance-based direction-wise loss stabilized through Tikhonov regularization and Cholesky decomposition. This direction-wise loss accounts for variance and correlation in the output space and, as we formally show, is equivalent to the L2-norm in a covariance-whitened space. Extensive experiments demonstrate that SKD consistently outperforms existing logit-based methods and even surpasses feature-based methods, despite its simpler design. Code is available at https://github.com/HyunJunSik/StreamLined.

cs.CV

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

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Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning

Sobol, Ido, Sohn, Kihyuk, Blum, Yoav, Zakharov, Egor, Bluvstein, Max, Vedaldi, Andrea, Litany, Or

原文摘要

We often aim to generate images that are both photorealistic and 3D-consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine-tuning an image generator, pre-trained on billions of real images, using renders of synthetic 3D assets, where annotations for control signals are available. While this approach can learn the desired controls, it often compromises the realism of the images due to domain gap between photographs and renders. We observe that this issue largely arises from the model learning an unintended association between the presence of control signals and the synthetic appearance of the images. To address this, we introduce Realiz3D, a lightweight framework for training diffusion models, that decouples controls and visual domain. The key idea is to explicitly learn visual domain, real or synthetic, separately from other control signals by introducing a co-variate that, fed into small residual adapters, shifts the domain. Then, the generator can be trained to gain controllability, without fitting to specific visual domain. In this way, the model can be guided to produce realistic images even when controls are applied. We enhance control transferability to the real domain by leveraging insights about roles of different layers and denoising steps in diffusion-based generators, informing new training and inference strategies that further mitigate the gap. We demonstrate the advantages of Realiz3D in tasks as text-to-multiview generation and texturing from 3D inputs, producing outputs that are 3D-consistent and photorealistic.

cs.CV

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

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OMG-Avatar: One-shot Multi-LOD Gaussian Head Avatar

Ren, Jianqiang, Liu, Lin, Hoi, Steven

原文摘要

We propose OMG-Avatar, a novel One-shot method that leverages a Multi-LOD (Level-of-Detail) Gaussian representation for animatable 3D head reconstruction from a single image in 0.2s. Our method enables LOD head avatar modeling using a unified model that accommodates diverse hardware capabilities and inference speed requirements. To capture both global and local facial characteristics, we employ a transformer-based architecture for global feature extraction and projection-based sampling for local feature acquisition. These features are effectively fused under the guidance of a depth buffer, ensuring occlusion plausibility. We further introduce a coarse-to-fine learning paradigm to support Level-of-Detail functionality and enhance the perception of hierarchical details. To address the limitations of 3DMMs in modeling non-head regions such as the shoulders, we introduce a multi-region decomposition scheme in which the head and shoulders are predicted separately and then integrated through cross-region combination. Extensive experiments demonstrate that OMG-Avatar outperforms state-of-the-art methods in reconstruction quality, reenactment performance, 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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