CustomCrafter: Customized Video Generation with Preserving Motion and
  Concept Composition Abilities
Authors: name: Tao Wu, name: Yong Zhang, name: Xintao Wang, name: Xianpan Zhou, name: Guangcong Zheng, name: Zhongang Qi, name: Ying Shan, name: Xi Li

Customized video generation aims to generate high-quality videos guided by
text prompts and subject's reference images. However, since it is only trained
on static images, the fine-tuning process of subject learning disrupts
abilities of video diffusion models (VDMs) to combine concepts and generate
motions. To restore these abilities, some methods use additional video similar
to the prompt to fine-tune or guide the model. This requires frequent changes
of guiding videos and even re-tuning of the model when generating different
motions, which is very inconvenient for users. In this paper, we propose
CustomCrafter, a novel framework that preserves the model's motion generation
and conceptual combination abilities without additional video and fine-tuning
to recovery. For preserving conceptual combination ability, we design a
plug-and-play module to update few parameters in VDMs, enhancing the model's
ability to capture the appearance details and the ability of concept
combinations for new subjects. For motion generation, we observed that VDMs
tend to restore the motion of video in the early stage of denoising, while
focusing on the recovery of subject details in the later stage. Therefore, we
propose Dynamic Weighted Video Sampling Strategy. Using the pluggability of our
subject learning modules, we reduce the impact of this module on motion
generation in the early stage of denoising, preserving the ability to generate
motion of VDMs. In the later stage of denoising, we restore this module to
repair the appearance details of the specified subject, thereby ensuring the
fidelity of the subject's appearance. Experimental results show that our method
has a significant improvement compared to previous methods.