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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2770050cc360d6db960cc7880351e480CVF · CVPR 2026

Refacade: Editing Object with Given Reference Texture

Huang, Youze, Ruan, Penghui, Zi, Bojia, Qi, Xianbiao, Wang, Jianan, Xiao, Rong

原文摘要

Recent advances in diffusion models have brought remarkable progress in image and video editing, yet some tasks remain underexplored. In this paper, we extend Object Retexture into video domain, which transfers local textures from a reference object to a target object in images or videos. To perform this task, a straightforward solution is to use ControlNet conditioned on the source structure and the reference texture. However, this approach suffers from limited controllability due to two reasons: conditioning on the raw reference image introduces unwanted structural information, and this method fails to disentangle visual texture and structure information of the source. To address this problem, we proposed a method, namely Refacade, that consists of two key designs to achieve precise and controllable texture transfer in both images and videos. First, we employ a texture remover trained on paired textured/untextured 3D mesh renderings to remove appearance information while preserving geometry and motion of source videos. Second, we disrupt the reference's global layout using a jigsaw permutation, encouraging the model to focus on local texture statistics rather than global layout of object. Extensive experiments demonstrate superior visual quality, precise editing, and controllability, outperforming strong baselines in both quantitative and human evaluations.

cs.CV

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

首次观测 · 记录 #1CVF · CVPR 2026:cvf:9a2acfdb08a046c3c92df282a8a41c5cCVF · CVPR 2026

Multi-Modal Representation Learning via Semi-Supervised Rate Reduction for Generalized Category Discovery

He, Wei, Meng, Xianghan, Huang, Zhiyuan, Qi, Xianbiao, Xiao, Rong, Li, Chun-Guang

原文摘要

Generalized Category Discovery (GCD) aims to identify both known and unknown categories, with only partial labels given for the known categories, posing a challenging open-set recognition problem. State-of-the-art approaches for GCD are usually built on multi-modality representation learning, which pays heavily attention upon inter-modality alignment rather than intra-modality alignment. In this paper, we propose a novel and effective multi-modal representation learning approach for GCD via Semi-Supervised Rate Reduction, called SSR^2-GCD, to learn cross-modality representations with desired underlying structure properties via properly harnessing intra-modality alignment. Moreover, to boost knowledge transfer, we integrate prompt candidates by leveraging the inter-modal alignment offered by Vision Language Models. We conduct extensive experiments on generic and fine-grained benchmark datasets, demonstrating superior performance of the proposed approach and verifying the importance of harnessing an proper intra-modality alignment.

cs.CV · cs.CL

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

首次观测 · 记录 #1CVF · CVPR 2026:cvf:d1f0ec94456f723d593e664110482442CVF · CVPR 2026

RoSAMDepth: Robust Self-supervised Depth Estimation Leveraging Segment Anything Model

Gao, Xuanang, Ning, Zhiwei, Zhang, Gengming, Cao, Jiaxi, Yang, Runze, Zheng, Zhonglong, Yang, Jie, Xiao, Rong 等 9 位作者

原文摘要

Robust depth estimation aims to maintain high-quality depths across diverse conditions. However, most existing methods estimate depth without taking into account the object-level information. As a result, the predicted depth may easily deviate within objects and become blurred under adverse conditions. To overcome this weakness, we propose RoSAMDepth, a novel framework that can assist robust self-supervised depth estimation in leveraging rich and diverse object-level priors from the Segment Anything Model (SAM). We focus on incorporating object-level information across three key aspects: a segment-guided representation contrasting method that injects object-level awareness into the feature representation space; an adaptive regional outlier masking strategy combined with a regional Gaussian likelihood loss that enforces regional depth smoothness; and an object-level reliability estimation strategy that mitigates the influence of unreliable supervision. Extensive experiments across multiple datasets and diverse weather conditions demonstrate that our method produces sharper, more accurate depth predictions, consistently outperforming state-of-the-art methods.

cs.CV

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