Text-to-video generation has made remarkable advancements through diffusion models. However, Multi-Concept Video Customization (MCVC) remains a significant challenge. We identify two key challenges for this task: 1) the identity decoupling issue, where directly adopting existing customization methods inevitably mix identity attributes when handling multiple concepts simultaneously, and 2) the scarcity of high-quality video-entity pairs, which is crucial for training a model that can well represent and decouple various customized concepts in video generation. To address these challenges, we introduce ConceptMaster, a novel framework that effectively addresses the identity decoupling issues while maintaining concept fidelity in video customization. Specifically, we propose to learn decoupled multi-concept embeddings and inject them into diffusion models in a standalone manner, which effectively guarantees the quality of customized videos with multiple identities, even for highly similar visual concepts. To overcome the scarcity of high-quality MCVC data, we establish a data construction pipeline, which enables collection of high-quality multi-concept video-entity data pairs across diverse scenarios. A multi-concept video evaluation set is further devised to comprehensively validate our method from three dimensions, including concept fidelity, identity decoupling ability, and video generation quality, across six different concept composition scenarios. Extensive experiments demonstrate that ConceptMaster significantly outperforms previous methods for video customization tasks, showing great potential to generate personalized and semantically accurate content for video diffusion models.
MotionAdapter: Video Motion Transfer via Content-Aware Attention Customization
Hangzhou Dianzi University（杭州电子科技大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；The Hong Kong University of Science and Technology（香港科技大学）；Ocean University of China（中国海洋大学）；Singapore Management University（新加坡管理大学）
OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
Johns Hopkins University（约翰霍普金斯大学）；Adobe Research（Adobe研究）；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）
MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Khalifa University（哈利法大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Jio Institute（Jio研究所）
JointTuner: Appearance-Motion Adaptive Joint Training for Customized Video Generation
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Alibaba International Digital Commerce（阿里巴巴国际数字商业）
From Noise to Nuance: Advances in Deep Generative Image Models
Georgia Institute of Technology（佐治亚理工学院）；AppCubic（AppCubic公司）；JTB Technology Corp.（JTB科技公司）；Indiana University（印第安纳大学）；Purdue University（普渡大学）；The University of Texas at Dallas（德州大学达拉斯分校）；University of Liverpool（利物浦大学）；Emory University（埃默里大学）；National Taiwan Normal University（台湾师范大学）
