title: ConceptMaster: Multi-Concept Video Customization on Diffusion
    Transformer Models Without Test-Time Tuning
summary: 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.
paper_authors._id: 678479ad0e7d90facee7ebfe
paper_authors.name: Yuzhou Huang
paper_authors.status: admin_assigned
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paper_authors.fullname: Yuzhou Huang
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paper_authors.name: Ziyang Yuan
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paper_authors.fullname: Ziyang Yuan
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paper_authors.user: ziyangy
paper_authors._id: 678479ad0e7d90facee7ec00
paper_authors.name: Quande Liu
paper_authors._id: 678479ad0e7d90facee7ec01
paper_authors.name: Qiulin Wang
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paper_authors.fullname: Wang Qiulin
paper_authors.type: user
paper_authors.user: Qiulin
paper_authors._id: 678479ad0e7d90facee7ec02
paper_authors.name: Xintao Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Xintao Wang
paper_authors.type: user
paper_authors.user: Xintao
paper_authors._id: 678479ad0e7d90facee7ec03
paper_authors.name: Ruimao Zhang
paper_authors.status: admin_assigned
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paper_authors._id: 65324848220f490dbaa3b8c9
paper_authors.avatarUrl: /avatars/cec7dcadf4657bb4f61e2c9a8075521b.svg
paper_authors.fullname: Zhang
paper_authors.type: user
paper_authors.user: Ruimao
paper_authors._id: 678479ad0e7d90facee7ec04
paper_authors.name: Pengfei Wan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T14:42:55.038Z
paper_authors._id: 662f93942510ef5735d7ad00
paper_authors.avatarUrl: /avatars/dc9486db75869ce902d0a638eea126bd.svg
paper_authors.fullname: magicwpf
paper_authors.type: user
paper_authors.user: magicwpf
paper_authors._id: 678479ad0e7d90facee7ec05
paper_authors.name: Di Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-13T09:38:41.879Z
paper_authors._id: 64bce15bafd1e46c5504ad38
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paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: di-zhang-fdu
paper_authors._id: 678479ad0e7d90facee7ec06
paper_authors.name: Kun Gai
paper_title: ConceptMaster: Multi-Concept Video Customization on Diffusion
    Transformer Models Without Test-Time Tuning
paper_summary: 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.
paper_ai_keywords: diffusion models text-to-video generation multi-Concept Video Customization (MCVC) identity decoupling problem high-quality video-entity pairs ConceptMaster Learning decoupled multi-concept embeddings concept fidelity identity decoupling ability video generation quality