Concat-ID: Towards Universal Identity-Preserving Video Synthesis
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.
67da71b1c26b43885226a72d
Yong Zhong
67da71b1c26b43885226a72e
Zhuoyi Yang
67da71b1c26b43885226a72f
Jiayan Teng
admin_assigned
2025-03-19T13:42:47.456Z
65228733377bffdc59a10117
/avatars/6eec07553658ab22f8058caa0bfbed49.svg
tengjiayan
user
tengjiayan
67da71b1c26b43885226a730
Xiaotao Gu
claimed_verified
2025-07-02T09:24:56.023Z
6438bca4a5e10f6d58694b47
/avatars/3aeb25fbc73c5cab1265e13d11adfb76.svg
XG
user
xgeric
67da71b1c26b43885226a731
Chongxuan Li
admin_assigned
2025-03-19T13:42:26.051Z
64c07b488e2612254361153b
/avatars/ade0f783cc4c2d3e73f402637f595471.svg
chongxuan li
user
zhenxuan00
Concat-ID: Towards Universal Identity-Preserving Video Synthesis
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.
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.
Variational Autoencoders 3D self-attention cross-video pairing strategy multi-stage training regimen identity-preserving video synthesis