A Self-Supervised Vision Transformer for 3D Motion Retrieval and its Application in Game Development

Abstract

The development of 3D contents such as games and movies is becoming larger and larger every year, and the cost of producing motion data for 3D characters accounts for a large percentage of the total cost. In order to streamline the production process, it is necessary to search for similar 3D motions. In this study, we propose a self-supervised Vision Transformer (ViT) for 3D motion retrieval. Using 3D motion data from actual game products, we compare the proposed method with existing CNN-based methods and supervised ViT, and show the improvement in accuracy. We also developed a 3D motion retrieval web system using the proposed method. In this study, we obtained feedback from motion creators who used the Web system in their actual game development flow and discuss it.