Stand-In: A Lightweight and Plug-and-Play Identity Control for Video
  Generation
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.
689aaeabfab6fdd2e52ac47c
Bowen Xue
claimed_verified
2025-08-14T13:39:40.376Z
6899c1c4c8d32e4cdea87215
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8plwumYYJa1TNkZvF1xoj.jpeg
Bowen Xue
user
BowenXue
689aaeabfab6fdd2e52ac47d
Qixin Yan
689aaeabfab6fdd2e52ac47e
Wenjing Wang
689aaeabfab6fdd2e52ac47f
Hao Liu
689aaeabfab6fdd2e52ac480
Chen Li
Stand-In: A Lightweight and Plug-and-Play Identity Control for Video
  Generation
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.
A lightweight framework for identity preservation in video generation using conditional image branches and restricted self-attentions outperforms full-parameter methods with minimal additional parameters.
conditional image branch pre-trained video generation model restricted self-attentions conditional position mapping subject-driven video generation pose-referenced video generation stylization face swapping