title: Subject-driven Video Generation via Disentangled Identity and Motion
summary: We propose to train a subject-driven customized video generation model
  through decoupling the subject-specific learning from temporal dynamics in
  zero-shot without additional tuning. A traditional method for video
  customization that is tuning-free often relies on large, annotated video
  datasets, which are computationally expensive and require extensive annotation.
  In contrast to the previous approach, we introduce the use of an image
  customization dataset directly on training video customization models,
  factorizing the video customization into two folds: (1) identity injection
  through image customization dataset and (2) temporal modeling preservation with
  a small set of unannotated videos through the image-to-video training method.
  Additionally, we employ random image token dropping with randomized image
  initialization during image-to-video fine-tuning to mitigate the copy-and-paste
  issue. To further enhance learning, we introduce stochastic switching during
  joint optimization of subject-specific and temporal features, mitigating
  catastrophic forgetting. Our method achieves strong subject consistency and
  scalability, outperforming existing video customization models in zero-shot
  settings, demonstrating the effectiveness of our framework.
paper_authors._id: 680ed2679e529f7799a0689f
paper_authors.name: Daneul Kim
paper_authors.status: claimed_verified
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paper_authors.fullname: Daneul Kim
paper_authors.type: user
paper_authors.user: carpedkm
paper_authors._id: 680ed2679e529f7799a068a0
paper_authors.name: Jingxu Zhang
paper_authors._id: 680ed2679e529f7799a068a1
paper_authors.name: Wonjoon Jin
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paper_authors.avatarUrl: /avatars/62c0887cfa7e1946ff32bcc2f44bf747.svg
paper_authors.fullname: Wonjoon Jin
paper_authors.type: user
paper_authors.user: Wonjoon-Jin
paper_authors._id: 680ed2679e529f7799a068a2
paper_authors.name: Sunghyun Cho
paper_authors.status: admin_assigned
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paper_authors.fullname: sunghyun cho
paper_authors.type: user
paper_authors.user: chosh1110
paper_authors._id: 680ed2679e529f7799a068a3
paper_authors.name: Qi Dai
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-28T00:57:12.914Z
paper_authors._id: 65115c00a588fdb36558b673
paper_authors.avatarUrl: /avatars/1f36263dc4bfaf696a4aa959a6aab1e1.svg
paper_authors.fullname: Qi Dai
paper_authors.type: user
paper_authors.user: daiqi
paper_authors._id: 680ed2679e529f7799a068a4
paper_authors.name: Jaesik Park
paper_authors._id: 680ed2679e529f7799a068a5
paper_authors.name: Chong Luo
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-28T00:57:12.914Z
paper_authors._id: 676a328148d749b7086782d0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Tt7u8l8f_1oVBWmBp7tkm.png
paper_authors.fullname: Chong Luo
paper_authors.type: user
paper_authors.user: cluo-ms
paper_title: Subject-driven Video Generation via Disentangled Identity and Motion
paper_summary: We propose to train a subject-driven customized video generation model
  through decoupling the subject-specific learning from temporal dynamics in
  zero-shot without additional tuning. A traditional method for video
  customization that is tuning-free often relies on large, annotated video
  datasets, which are computationally expensive and require extensive annotation.
  In contrast to the previous approach, we introduce the use of an image
  customization dataset directly on training video customization models,
  factorizing the video customization into two folds: (1) identity injection
  through image customization dataset and (2) temporal modeling preservation with
  a small set of unannotated videos through the image-to-video training method.
  Additionally, we employ random image token dropping with randomized image
  initialization during image-to-video fine-tuning to mitigate the copy-and-paste
  issue. To further enhance learning, we introduce stochastic switching during
  joint optimization of subject-specific and temporal features, mitigating
  catastrophic forgetting. Our method achieves strong subject consistency and
  scalability, outperforming existing video customization models in zero-shot
  settings, demonstrating the effectiveness of our framework.
paper_ai_summary: A method for zero-shot video customization achieves strong performance by decoupling subject-specific learning from temporal dynamics using image customization datasets and randomization techniques.
paper_ai_keywords: identity injection image customization dataset temporal modeling image-to-video training random image token dropping stochastic switching catastrophic forgetting subject consistency scalability zero-shot settings