In recent years, with the rapid development of artificial intelligence and computer vision technologies, the interaction between humans and computers has become increasingly vivid and expressive. Particularly in the field of animation production, how to generate dynamic videos from static images has been a hot research topic.
Recently, a new technology called "DisPose" has emerged, which achieves more controllable character animation effects by decoupling pose guidance. Simply put, DisPose allows a reference character to perform actions from an input action video.
The core of the DisPose technology lies in its reconstruction and utilization of traditional sparse pose information. Traditional methods often rely on sparse skeletal pose guidance, which frequently fails to provide sufficient control signals during dynamic video generation, resulting in less refined animation effects. To address this shortcoming, DisPose proposes a novel approach that transforms sparse pose information into motion field guidance and keypoint correspondences, enabling more detailed motion generation.
Specifically, DisPose first calculates a sparse motion field from the skeletal pose and introduces a method for generating a dense motion field based on the reference image. This approach not only provides motion signals at the regional level but also maintains the generality of sparse pose control. Additionally, DisPose extracts diffusion features corresponding to pose keypoints from the reference image, and through the computation of multi-scale point correspondences, these features are transmitted to the target pose to enhance appearance consistency.
To smoothly integrate this innovative technology into existing models, the researchers also proposed a plug-in hybrid ControlNet architecture. This architecture improves the quality and consistency of the generated videos without altering the existing model parameters. Through extensive qualitative and quantitative experiments, DisPose demonstrates significant advantages over current technologies, indicating the future development direction of animation production technology.
DisPose enhances the expressiveness and controllability of character animation by optimizing the utilization of pose information. This advancement is not only significant in academic research but also brings new possibilities for the future animation industry.
Project link: https://lihxxx.github.io/DisPose/
Key Points:
📍 DisPose is a new character animation technology that achieves more precise dynamic generation by decoupling pose guidance.
🎨 This technology transforms sparse pose information into motion field guidance and keypoint correspondences, providing detailed motion signals.
🔧 The hybrid ControlNet architecture proposed by researchers effectively improves the quality and consistency of generated videos.
