VideoAnydoor: High-fidelity Video Object Insertion with Precise Motion
  Control
Authors: name: Yuanpeng Tu, name: Hao Luo, name: Xi Chen, name: Sihui Ji, name: Xiang Bai, name: Hengshuang Zhao

Despite significant advancements in video generation, inserting a given
object into videos remains a challenging task. The difficulty lies in
preserving the appearance details of the reference object and accurately
modeling coherent motions at the same time. In this paper, we propose
VideoAnydoor, a zero-shot video object insertion framework with high-fidelity
detail preservation and precise motion control. Starting from a text-to-video
model, we utilize an ID extractor to inject the global identity and leverage a
box sequence to control the overall motion. To preserve the detailed appearance
and meanwhile support fine-grained motion control, we design a pixel warper. It
takes the reference image with arbitrary key-points and the corresponding
key-point trajectories as inputs. It warps the pixel details according to the
trajectories and fuses the warped features with the diffusion U-Net, thus
improving detail preservation and supporting users in manipulating the motion
trajectories. In addition, we propose a training strategy involving both videos
and static images with a reweight reconstruction loss to enhance insertion
quality. VideoAnydoor demonstrates significant superiority over existing
methods and naturally supports various downstream applications (e.g., talking
head generation, video virtual try-on, multi-region editing) without
task-specific fine-tuning.