In recent years, AI technology has developed rapidly, especially in the field of animation production. Generating dynamic videos based on static images has become a research hotspot. Traditional animation production methods often rely on sparse skeletal posture information, resulting in animation effects that are not precise enough. In order to solve this problem, new technologies are constantly emerging, striving to achieve more precise and controllable character image animation.
In recent years, with the rapid development of artificial intelligence and computer vision technology, the interaction between humans and computers has become more and more vivid and expressive. Especially in the field of animation production, how to generate dynamic videos based on static images has always been a hot research topic.
Recently, a new technology called "DisPose" has emerged, which achieves more controllable character image animation effects through decoupled posture guidance. Simply put, DisPose enables the input of action videos and reference characters, allowing the reference characters to realize the actions in the video.
The core of DisPose technology lies in its reconstruction and utilization of traditional sparse pose information. Traditional methods mostly rely on sparse skeleton pose guidance, which often cannot provide sufficient control signals when dynamically generating videos, resulting in insufficiently detailed animation effects. To make up for this shortcoming, DisPose proposes a brand new method to achieve more detailed motion generation by converting sparse pose information into sports field guidance and key point correspondences.
Specifically, DisPose first calculates sparse motion fields for skeletal poses and introduces a dense motion field generation method based on reference images. This approach not only provides regional-level motion signals but also maintains the universality of sparse attitude control. At the same time, DisPose also extracts diffusion features corresponding to pose key points from the reference image, and then transfers these features to the target pose by calculating multi-scale point correspondences to enhance the consistency of appearance.
In order to enable this innovative technology to be smoothly integrated into existing models, the researchers also proposed a plug-in hybrid ControlNet architecture. This architecture improves the quality and consistency of generated videos without changing existing model parameters. Through extensive qualitative and quantitative experiments, DisPose demonstrates significant advantages over current technologies and heralds the future direction of animation production technology.
DisPose improves the expressiveness and controllability of portrait animation by optimizing the use of posture information. This progress is not only of great significance in academic research, but also brings new possibilities to the future animation industry.
Project entrance: https://lihxxx.github.io/DisPose/
Highlights:
DisPose is a new portrait animation technology that enables more precise dynamic generation through decoupled pose guidance.
This technology converts sparse posture information into motion field guidance and key point correspondence, providing detailed motion signals.
The hybrid ControlNet architecture proposed by the researchers can effectively improve the quality and consistency of generated videos.
The emergence of DisPose technology marks a new milestone in animation production technology. Its efficient gesture information processing method and innovative hybrid ControlNet architecture provide powerful technical support for more realistic and detailed portrait animation production in the future, and also bring unlimited possibilities to the animation industry. We look forward to DisPose playing a greater role in animation production in the future.