title: Concat-ID: Towards Universal Identity-Preserving Video Synthesis
summary: We present Concat-ID, a unified framework for identity-preserving video
  generation. Concat-ID employs Variational Autoencoders to extract image
  features, which are concatenated with video latents along the sequence
  dimension, leveraging solely 3D self-attention mechanisms without the need for
  additional modules. A novel cross-video pairing strategy and a multi-stage
  training regimen are introduced to balance identity consistency and facial
  editability while enhancing video naturalness. Extensive experiments
  demonstrate Concat-ID's superiority over existing methods in both single and
  multi-identity generation, as well as its seamless scalability to multi-subject
  scenarios, including virtual try-on and background-controllable generation.
  Concat-ID establishes a new benchmark for identity-preserving video synthesis,
  providing a versatile and scalable solution for a wide range of applications.
paper_authors._id: 67da71b1c26b43885226a72d
paper_authors.name: Yong Zhong
paper_authors._id: 67da71b1c26b43885226a72e
paper_authors.name: Zhuoyi Yang
paper_authors._id: 67da71b1c26b43885226a72f
paper_authors.name: Jiayan Teng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:42:47.456Z
paper_authors._id: 65228733377bffdc59a10117
paper_authors.avatarUrl: /avatars/6eec07553658ab22f8058caa0bfbed49.svg
paper_authors.fullname: tengjiayan
paper_authors.type: user
paper_authors.user: tengjiayan
paper_authors._id: 67da71b1c26b43885226a730
paper_authors.name: Xiaotao Gu
paper_authors._id: 67da71b1c26b43885226a731
paper_authors.name: Chongxuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:42:26.051Z
paper_authors._id: 64c07b488e2612254361153b
paper_authors.avatarUrl: /avatars/ade0f783cc4c2d3e73f402637f595471.svg
paper_authors.fullname: chongxuan li
paper_authors.type: user
paper_authors.user: zhenxuan00
paper_title: Concat-ID: Towards Universal Identity-Preserving Video Synthesis
paper_summary: We present Concat-ID, a unified framework for identity-preserving video
  generation. Concat-ID employs Variational Autoencoders to extract image
  features, which are concatenated with video latents along the sequence
  dimension, leveraging solely 3D self-attention mechanisms without the need for
  additional modules. A novel cross-video pairing strategy and a multi-stage
  training regimen are introduced to balance identity consistency and facial
  editability while enhancing video naturalness. Extensive experiments
  demonstrate Concat-ID's superiority over existing methods in both single and
  multi-identity generation, as well as its seamless scalability to multi-subject
  scenarios, including virtual try-on and background-controllable generation.
  Concat-ID establishes a new benchmark for identity-preserving video synthesis,
  providing a versatile and scalable solution for a wide range of applications.
paper_ai_summary: Concat-ID uses Variational Autoencoders and 3D self-attention to generate identity-preserving videos, offering superior scalability and naturalness in both single and multi-identity scenarios.
paper_ai_keywords: Variational Autoencoders 3D self-attention cross-video pairing strategy multi-stage training regimen identity-preserving video synthesis