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arXiv · 2503.14151

Concat-ID: Towards Universal Identity-Preserving Video Synthesis

Yong Zhong·Zhuoyi Yang·Jiayan Teng·Xiaotao Gu·Chongxuan Li
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Abstract

We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs variational autoencoders to extract image features, which are then concatenated with video latents along the sequence dimension. It relies exclusively on inherent 3D self-attention mechanisms to incorporate them, eliminating the need for additional parameters or 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.

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Yong Zhong, Zhuoyi Yang, Jiayan Teng, Xiaotao Gu, Chongxuan Li. 2025-03-18. Concat-ID: Towards Universal Identity-Preserving Video Synthesis. https://arxiv.org/abs/2503.14151

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