CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for
  Zero-Shot Customized Video Diffusion Transformers
Authors: name: D. She, name: Mushui Liu, name: Jingxuan Pang, name: Jin Wang, name: Zhen Yang, name: Wanggui He, name: Guanghao Zhang, name: Yi Wang, name: Qihan Huang, name: Haobin Tang, name: Yunlong Yu, name: Siming Fu

Customized generation has achieved significant progress in image synthesis,
yet personalized video generation remains challenging due to temporal
inconsistencies and quality degradation. In this paper, we introduce
CustomVideoX, an innovative framework leveraging the video diffusion
transformer for personalized video generation from a reference image.
CustomVideoX capitalizes on pre-trained video networks by exclusively training
the LoRA parameters to extract reference features, ensuring both efficiency and
adaptability. To facilitate seamless interaction between the reference image
and video content, we propose 3D Reference Attention, which enables direct and
simultaneous engagement of reference image features with all video frames
across spatial and temporal dimensions. To mitigate the excessive influence of
reference image features and textual guidance on generated video content during
inference, we implement the Time-Aware Reference Attention Bias (TAB) strategy,
dynamically modulating reference bias over different time steps. Additionally,
we introduce the Entity Region-Aware Enhancement (ERAE) module, aligning highly
activated regions of key entity tokens with reference feature injection by
adjusting attention bias. To thoroughly evaluate personalized video generation,
we establish a new benchmark, VideoBench, comprising over 50 objects and 100
prompts for extensive assessment. Experimental results show that CustomVideoX
significantly outperforms existing methods in terms of video consistency and
quality.