Temporal Parameter-free Deep Skinning of Animated Meshes

TL;DR

This paper introduces a deep learning-based method for mesh animation compression that assigns vertices to clusters and derives skinning weights with minimal error, without requiring parameter tuning.

Contribution

It presents a novel parameter-free deep learning approach for skinning weight derivation that improves accuracy and efficiency over previous clustering methods.

Findings

Lower approximation error compared to previous methods

Fewer iterations needed for optimal transformation and vertex set

No user-tuned parameters required during compression

Abstract

In computer graphics, animation compression is essential for efficient storage, streaming and reproduction of animated meshes. Previous work has presented efficient techniques for compression by deriving skinning transformations and weights using clustering of vertices based on geometric features of vertices over time. In this work we present a novel approach that assigns vertices to bone-influenced clusters and derives weights using deep learning through a training set that consists of pairs of vertex trajectories (temporal vertex sequences) and the corresponding weights drawn from fully rigged animated characters. The approximation error of the resulting linear blend skinning scheme is significantly lower than the error of competent previous methods by producing at the same time a minimal number of bones. Furthermore, the optimal set of transformation and vertices is derived in fewer iterations due to the better initial positioning in the multidimensional variable space. Our method requires no parameters to be determined or tuned by the user during the entire process of compressing a mesh animation sequence.