ConceptMaster: Multi-Concept Video Customization on Diffusion
  Transformer Models Without Test-Time Tuning
Authors: name: Yuzhou Huang, name: Ziyang Yuan, name: Quande Liu, name: Qiulin Wang, name: Xintao Wang, name: Ruimao Zhang, name: Pengfei Wan, name: Di Zhang, name: Kun Gai

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 in this task: 1) the identity
decoupling problem, where directly adopting existing customization methods
inevitably mix attributes when handling multiple concepts simultaneously, and
2) the scarcity of high-quality video-entity pairs, which is crucial for
training such a model that represents and decouples various concepts well. To
address these challenges, we introduce ConceptMaster, an innovative framework
that effectively tackles the critical issues of identity decoupling while
maintaining concept fidelity in customized videos. Specifically, we introduce a
novel strategy of learning decoupled multi-concept embeddings that are injected
into the diffusion models in a standalone manner, which effectively guarantees
the quality of customized videos with multiple identities, even for highly
similar visual concepts. To further overcome the scarcity of high-quality MCVC
data, we carefully establish a data construction pipeline, which enables
systematic collection of precise multi-concept video-entity data across diverse
concepts. A comprehensive benchmark is designed to validate the effectiveness
of our model from three critical dimensions: concept fidelity, identity
decoupling ability, and video generation quality across six different concept
composition scenarios. Extensive experiments demonstrate that our ConceptMaster
significantly outperforms previous approaches for this task, paving the way for
generating personalized and semantically accurate videos across multiple
concepts.