ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
This work addresses the need for advanced animation tools, though it is incremental as it builds on existing neural methods for pose modeling.
The paper tackles the problem of constructing a full human pose from sparse user inputs for AI-assisted animation, proposing a neural architecture that outperforms a Transformer baseline in accuracy and efficiency.
Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling. Specifically, we tackle the problem of constructing a full static human pose based on sparse and variable user inputs (e.g. locations and/or orientations of a subset of body joints). To solve this problem, we propose a novel neural architecture that combines residual connections with prototype encoding of a partially specified pose to create a new complete pose from the learned latent space. We show that our architecture outperforms a baseline based on Transformer, both in terms of accuracy and computational efficiency. Additionally, we develop a user interface to integrate our neural model in Unity, a real-time 3D development platform. Furthermore, we introduce two new datasets representing the static human pose modeling problem, based on high-quality human motion capture data, which will be released publicly along with model code.