Stand-In: A Lightweight and Plug-and-Play Identity Control for Video
  Generation
Authors: name: Bowen Xue, name: Qixin Yan, name: Wenjing Wang, name: Hao Liu, name: Chen Li

Generating high-fidelity human videos that match user-specified identities is
important yet challenging in the field of generative AI. Existing methods often
rely on an excessive number of training parameters and lack compatibility with
other AIGC tools. In this paper, we propose Stand-In, a lightweight and
plug-and-play framework for identity preservation in video generation.
Specifically, we introduce a conditional image branch into the pre-trained
video generation model. Identity control is achieved through restricted
self-attentions with conditional position mapping, and can be learned quickly
with only 2000 pairs. Despite incorporating and training just sim1\%
additional parameters, our framework achieves excellent results in video
quality and identity preservation, outperforming other full-parameter training
methods. Moreover, our framework can be seamlessly integrated for other tasks,
such as subject-driven video generation, pose-referenced video generation,
stylization, and face swapping.