Papers › GenMotion: Data-driven Motion Generators for Real-time Animation Synthesis
GenMotion: Data-driven Motion Generators for Real-time Animation Synthesis
Yizhou Zhao, Wensi Ai, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu
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With the recent success of deep learning algorithms, many researchers have focused on generative models for human motion animation. However, the research community lacks a platform for training and benchmarking various algorithms, and the animation industry needs a toolkit for implementing advanced motion synthesizing techniques. To facilitate the study of deep motion synthesis methods for skeleton-based human animation and their potential applications in practical animation making, we introduce \genmotion: a library that provides unified pipelines for data loading, model training, and animation sampling with various deep learning algorithms. Besides, by combining Python coding in the animation software \genmotion\ can assist animators in creating real-time 3D character animation. Source code is available at https://github.com/realvcla/GenMotion/.
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