CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects
Zhao Wang Aoxue Li Enze Xie Lingting Zhu Yong Guo Qi Dou Zhenguo Li
Customized text-to-video generation aims to generate high-quality videos
guided by text prompts and subject references. Current approaches designed for
single subjects suffer from tackling multiple subjects, which is a more
challenging and practical scenario. In this work, we aim to promote
multi-subject guided text-to-video customization. We propose CustomVideo, a
novel framework that can generate identity-preserving videos with the guidance
of multiple subjects. To be specific, firstly, we encourage the co-occurrence
of multiple subjects via composing them in a single image. Further, upon a
basic text-to-video diffusion model, we design a simple yet effective attention
control strategy to disentangle different subjects in the latent space of
diffusion model. Moreover, to help the model focus on the specific object area,
we segment the object from given reference images and provide a corresponding
object mask for attention learning. Also, we collect a multi-subject
text-to-video generation dataset as a comprehensive benchmark, with 69
individual subjects and 57 meaningful pairs. Extensive qualitative,
quantitative, and user study results demonstrate the superiority of our method,
compared with the previous state-of-the-art approaches.