Text-guided texture editing aims to modify object appearance while preserving the underlying geometric structure. However, our empirical analysis reveals that even SOTA editing models frequently struggle to maintain structural consistency during texture editing, despite the intended changes being purely appearance-related. Motivated by this observation, we jointly enhance structure preservation from both data and training perspectives, and build TexEditor, a dedicated texture editing model based on Qwen-Image-Edit-2509. Firstly, we construct TexBlender, a high-quality SFT dataset generated with Blender, which provides strong structural priors for a cold start. Sec- ondly, we introduce StructureNFT, a RL-based approach that integrates structure-preserving losses to transfer the structural priors learned during SFT to real-world scenes. Moreover, due to the limited realism and evaluation coverage of existing benchmarks, we introduce TexBench, a general-purpose real-world benchmark for text-guided texture editing. Extensive experiments on existing Blender-based texture benchmarks and our TexBench show that TexEditor consistently outperforms strong baselines such as Nano Banana Pro. In addition, we assess TexEditor on the general purpose benchmark ImgEdit to validate its generalization. Our code and data are available at https://github.com/KlingAIResearch/TexEditor.
University of Science and Technology of China（中国科学技术大学）；Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；Nanjing University（南京大学）；Computer Vision Center, Universitat Autònoma（巴塞罗那自治大学计算机视觉中心）；Alibaba Group（阿里巴巴集团）；VCIP, CS, Nankai University（南开大学电子信息与光学工程学院（注：VCIP为视频与图像处理方向，此处按原文机构提取））；Ant Group（蚂蚁集团）；Institute for Intelligent Computing, Alibaba Group（阿里巴巴集团智能计算研究院）
Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing
Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Nanjing University（南京大学）；State Key Lab of CAD & CG, Zhejiang University（浙江大学计算机辅助设计与图形学国家重点实验室）；Shenzhen Loop Area Institute（深圳河套学院）
Refaçade: Editing Object with Given Reference Texture
University of Electronic Science and Technology of China（电子科技大学）；The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；IntelliFusion Inc.（云天励飞公司）；Astribot Inc.（Astribot公司）
PointGT: Simultaneous Geometry and Texture Editing for Point-Based Representations
Simon Fraser University（西蒙菲莎大学）；Google DeepMind（谷歌DeepMind）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究院）
TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images
University of California, San Diego（加州大学圣地亚哥分校）；Google Research（谷歌研究院）
神经领域（NFs）最近的工作是从特定于类的单视图图像集合中学习3D表示。然而，它们无法重建保留高频细节的输入数据。此外，这些方法不能将外观与几何图形分离，因此不适合于纹理转换和编辑等任务。在这项工作中，我们提出了TEGLO（纹理EG 3D-GLO）的学习3D表示从单一视图在野外图像集合为给定的一类对象。我们通过训练条件神经辐射场（NeRF）来实现这一点，而无需任何显式的3D监督。我们通过创建一个密集的对应映射到一个二维规范空间，我们的方法与编辑功能。我们证明，这样的映射，使纹理传输和纹理编辑，而不需要网格共享拓扑结构。我们的关键见解是，通过将输入图像像素映射到纹理空间，我们可以实现近乎完美的重建（在1024^2分辨率下>= 74 dB PSNR）。我们的配方允许高品质的3D一致的新颖的视图合成与高频率的细节在百万像素的图像分辨率。
DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image Editing
Shenzhen Technology University（深圳科技大学）；University of Washington（华盛顿大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
ZeroScene: A Zero-Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editing
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Pengcheng Laboratory, China（鹏城实验室）；Harbin Institute of Technology, China（哈尔滨工业大学）；Harbin Institute of Technology, Suzhou Research Institute, China（哈尔滨工业大学苏州市研究院）
Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing
Media Integration and Communication Center (MICC), University of Florence（佛罗伦萨大学媒体集成与传播中心（MICC））；University of Florence（佛罗伦萨大学）；University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）；University of Pisa（比萨大学）
SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing
South China University of Technology（华南理工大学）；Zhejiang University（浙江大学）；Huawei Cloud Computing Technologies Co., Ltd（华为云计算技术有限公司）；Peking University（北京大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
Miyang Technology (Shanghai) Co., Ltd.（米扬科技（上海）有限公司）；Key Laboratory of System Software (Chinese Academy of Sciences)（中国科学院系统软件重点实验室）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；Tianjin University（天津大学）
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；South China University of Technology（华南理工大学）；The University of Hong Kong（香港大学）；Tencent AI Lab（腾讯AI实验室）
FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing
School of Computer and Communication Sciences, EPFL（EPFL计算机与通信科学学院）；Department of Electrical and Photonics Engineering, DTU（丹麦技术大学电气与光子工程系）；Department of Electrical Engineering, Chalmers University of Technology（查尔姆斯理工大学电气工程系）
CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam（越南科学大学信息科技学院）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学）；Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia（莫纳什大学信息科技学院）；Department of Computer Science, University of Dayton, Dayton, Ohio, US（Dayton 大学计算机科学系）
University of Surrey（萨里大学）；The University of Hong Kong（香港大学）；Imperial College London（帝国理工学院）；iFlyTek-Surrey Joint Research Centre on AI（科大讯飞-萨里人工智能联合研究中心）；Surrey Institute for People-Centred AI（萨里以人为本人工智能研究所）
