ConceptMaster: Multi-Concept Video Customization on Diffusion
  Transformer Models Without Test-Time Tuning
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.
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Yuzhou Huang
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2025-01-13T09:37:29.216Z
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Yuzhou Huang
user
Yuzhou914
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Ziyang Yuan
admin_assigned
2025-01-13T09:37:35.767Z
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Ziyang Yuan
user
ziyangy
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Quande Liu
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Qiulin Wang
admin_assigned
2025-01-13T09:37:54.749Z
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Wang Qiulin
user
Qiulin
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Xintao Wang
admin_assigned
2025-01-13T09:38:01.080Z
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Xintao Wang
user
Xintao
678479ad0e7d90facee7ec03
Ruimao Zhang
admin_assigned
2025-01-13T09:38:09.610Z
65324848220f490dbaa3b8c9
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Zhang
user
Ruimao
678479ad0e7d90facee7ec04
Pengfei Wan
claimed_verified
2025-03-17T14:42:55.038Z
662f93942510ef5735d7ad00
https://cdn-avatars.huggingface.co/v1/production/uploads/662f93942510ef5735d7ad00/ZIDIPm63sncIHFTT5b0uR.png
magicwpf
user
magicwpf
678479ad0e7d90facee7ec05
Di Zhang
admin_assigned
2025-01-13T09:38:41.879Z
64bce15bafd1e46c5504ad38
https://cdn-avatars.huggingface.co/v1/production/uploads/64bce15bafd1e46c5504ad38/vkEjiu-mIagKlrXzDH75o.png
Di Zhang
user
di-zhang-fdu
678479ad0e7d90facee7ec06
Kun Gai
ConceptMaster: Multi-Concept Video Customization on Diffusion
  Transformer Models Without Test-Time Tuning
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.
ConceptMaster addresses identity decoupling and data scarcity in text-to-video generation using decoupled multi-concept embeddings and a comprehensive data pipeline, outperforming previous methods.
diffusion models Multi-Concept Video Customization (MCVC) identity decoupling concept fidelity multi-concept embeddings high-quality video-entity pairs data construction pipeline concept composition video generation quality