CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

TL;DR

CharacterGAN is a few-shot generative model for character animation that uses layered keypoint processing and adaptive features to produce realistic, interactive poses with limited training data.

Contribution

The paper introduces a novel layered keypoint approach and adaptive scaling method for few-shot character animation, addressing occlusions and out-of-distribution poses.

Findings

Outperforms recent baselines in realism and diversity

Handles discrete state changes effectively

Scales to larger datasets with more data

Abstract

We introduce CharacterGAN, a generative model that can be trained on only a few samples (8 - 15) of a given character. Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation. Since we only have very limited training samples, one of the key challenges lies in how to address (dis)occlusions, e.g. when a hand moves behind or in front of a body. To address this, we introduce a novel layering approach which explicitly splits the input keypoints into different layers which are processed independently. These layers represent different parts of the character and provide a strong implicit bias that helps to obtain realistic results even with strong (dis)occlusions. To combine the features of individual layers we use an adaptive scaling approach conditioned on all keypoints. Finally, we introduce a mask connectivity constraint to reduce distortion artifacts that occur with extreme out-of-distribution poses at test time. We show that our approach outperforms recent baselines and creates realistic animations for diverse characters. We also show that our model can handle discrete state changes, for example a profile facing left or right, that the different layers do indeed learn features specific for the respective keypoints in those layers, and that our model scales to larger datasets when more data is available.