Concat-ID: Towards Universal Identity-Preserving Video Synthesis
Authors: name: Yong Zhong, name: Zhuoyi Yang, name: Jiayan Teng, name: Xiaotao Gu, name: Chongxuan Li

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.