sebastianstarke / AI4Animation
This project explores the opportunities of deep learning for character animation and control as part of my Ph.D. research at the University of Edinburgh in the School of Informatics, supervised by Taku Komura. Over the last couple years, this project has become a modular and stable framework for data-driven character animation, including data processing, network training and runtime control, developed in Unity3D / Tensorflow / PyTorch. This repository enables using neural networks for animating biped locomotion, quadruped locomotion, and character-scene interactions with objects and the environment, plus sports games. Further advances on this research will continue being added to this project.
Interactively synthesizing novel combinations and variations of character movements from different motion skills is a key problem in computer animation. In this research, we propose a deep learning framework to produce a large variety of martial arts movements in a controllable manner from raw motion capture data. Our method imitates animation layering using neural networks with the aim to overcome typical challenges when mixing, blending and editing movements from unaligned motion sources. The system can be used for offline and online motion generation alike, provides an intuitive interface to integrate with animator workflows, and is relevant for real-time applications such as computer games.
Not sure how to align complex character movements? Tired of phase labeling? Unclear how to squeeze everything into a single phase variable? Don't worry, a solution exists!
