Learning-based pose edition for efficient and interactive design

TL;DR

This paper introduces a neural network-based method for automatic constraint learning from motion capture data, enabling intuitive and efficient pose editing for character animation accessible to nonexperts.

Contribution

It presents a novel data-driven approach that leverages neural networks to learn pose constraints, improving ease of use and realism in character animation.

Findings

Neural networks effectively learn pose constraints from motion capture data.

The proposed tool allows nonexperts to intuitively manipulate character poses.

The method enhances animation realism and user accessibility.

Abstract

Authoring an appealing animation for a virtual character is a challenging task. In computer-aided keyframe animation artists define the key poses of a character by manipulating its underlying skeletons. To look plausible, a character pose must respect many ill-defined constraints, and so the resulting realism greatly depends on the animator's skill and knowledge. Animation software provide tools to help in this matter, relying on various algorithms to automatically enforce some of these constraints. The increasing availability of motion capture data has raised interest in data-driven approaches to pose design, with the potential of shifting more of the task of assessing realism from the artist to the computer, and to provide easier access to nonexperts. In this article, we propose such a method, relying on neural networks to automatically learn the constraints from the data. We describe an efficient tool for pose design, allowing na{\"i}ve users to intuitively manipulate a pose to create character animations.