  Customized text-to-video generation aims to generate high-quality videos
guided by text prompts and subject references. Current approaches for
personalizing text-to-video generation suffer from tackling multiple subjects,
which is a more challenging and practical scenario. In this work, our aim is 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 area of the object, 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 63 individual subjects from 13 different categories and 68
meaningful pairs. Extensive qualitative, quantitative, and user study results
demonstrate the superiority of our method compared to previous state-of-the-art
approaches. The project page is https://kyfafyd.wang/projects/customvideo.
