Text-to-Image (T2I) diffusion models have recently gained traction for their versatility and user-friendliness in 2D content generation and editing. However, training a diffusion model specifically for 3D scene editing is challenging due to the scarcity of large-scale datasets. Currently, editing 3D scenes necessitates either retraining the model to accommodate various 3D edits or developing specific methods tailored to each unique editing type. Moreover, state-of-the-art (SOTA) techniques require multiple synchronized edited images from the same scene to enable effective scene editing. Given the current limitations of T2I models, achieving consistent editing effects across multiple images remains difficult, leading to multi-view inconsistency in editing. This inconsistency undermines the performance of 3D scene editing when these images are utilized. In this study, we introduce a novel, training-free 3D scene editing technique called \textsc{Free-Editor}, which enables users to edit 3D scenes without the need for model retraining during the testing phase. Our method effectively addresses the issue of multi-view style inconsistency found in state-of-the-art (SOTA) methods through the implementation of a single-view editing scheme. Specifically, we demonstrate that editing a particular 3D scene can be achieved by modifying only a single view. To facilitate this, we present an Edit Transformer that ensures intra-view consistency and inter-view style transfer using self-view and cross-view attention mechanisms, respectively. By eliminating the need for model retraining and multi-view editing, our approach significantly reduces editing time and memory resource requirements, achieving runtimes approximately 20 times faster than SOTA methods. We have performed extensive experiments on various benchmark datasets, showcasing the diverse editing capabilities of our proposed technique.
VRWKV-Editor: Reducing quadratic complexity in transformer-based video editing
International Artificial Intelligence Center of Morocco University Mohammed VI Polytechnic Rabat, Morocco（摩洛哥国际人工智能中心摩洛哥穆莱·伊沙克·穆莱·阿卜杜勒阿齐兹 polytechnic 大学拉巴特分校）；University Mohammed VI Polytechnic Rabat, Morocco（摩洛哥穆莱·伊沙克·穆莱·阿卜杜勒阿齐兹 polytechnic 大学拉巴特分校）；Sorbonne University, LIP6 - UMR 7606 CNRS, France（索邦大学，LIP6 - UMR 7606 CNRS，法国）
DisCo3D: Distilling Multi-View Consistency for 3D Scene Editing
Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University（人工智能研究院、北京信息科学与技术国家研究中心、计算机科学与技术系、清华大学）；College of Computer, National University of Defense Technology（计算机学院、国防科技大学）
Why Do DiT Editors Drift? Plug-and-Play Low Frequency Alignment in VAE Latent Space
Tsinghua University（清华大学）；Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；CASIA；University of Electronic Science and Technology of China（电子科技大学）；Guangming Laboratory（光明实验室）
SceneCraft: Interactive System for Image Editing via Scene Graph
University of Science, Ho Chi Minh, Vietnam（胡志明市理科大学）；Vietnam National University, Ho Chi Minh, Vietnam（越南国家大学胡志明市）；University of Dayton, Dayton, Ohio, USA（代顿大学）
