@inproceedings{
zhang2024dragentitytrajectory,
title={DragEntity:Trajectory Guided Video Generation using Entity and Positional Relationships},
author={Wan Zhang and Sheng Tang and Jiawei Wei and Ruize Zhang and Juan Cao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=H4E32wjshc}
}
In recent years, diffusion models have achieved tremendous success in the field of video generation, with controllable video generation receiving significant attention. However, existing control methods still face two limitations: Firstly, control conditions (such as depth maps, 3D Mesh) are difficult for ordinary users to obtain directly. Secondly, it’s challenging to drive multiple objects through complex motions with multiple trajectories simultaneously. In this paper, we introduce DragEntity, a video generation model that utilizes entity representation for controlling the motion of multiple objects. In comparison to previous methods, MotionCtrl offers two main advantages: 1) Trajectory-based methods are more user-friendly for interaction. Users only need to draw trajectories during the interaction to generate videos. 2) We use entity representation to represent any object in the image, and multiple objects can maintain relative spatial relationships. Therefore, we allow multiple trajectories to control multiple objects in the image with different levels of complexity simultaneously. Our experiments validate the effectiveness of DragEntity, demonstrating its superior performance in fine-grained control in video generation.
DragEntity:Trajectory Guided Video Generation using Entity and Positional Relationships
Wan Zhang Sheng Tang Jiawei Wei Ruize Zhang Juan Cao