Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization

2023/06/15 by Makito Kobayashi, Chen-Chieh Liao, Kobayashi, Makito +7 · 6 citations
Computer Science · Engineering · #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Motion and Animation #Human Pose and Action Recognition

paper · pdf · doi:10.48550/arxiv.2306.08861

openalex publication_date 2023/06/15 · openalex created_date 2023/06/17 · openalex updated_date 2026/07/28

Abstract

In this work, we proposed a new style-diverse dataset for the domain of motion style transfer. The motion dataset uses an industrial-standard human bone structure and thus is industry-ready to be plugged into 3D characters for many projects. We claim the challenges in motion style transfer and encourage future work in this domain by releasing the proposed motion dataset both to the public and the market. We conduct a comprehensive study on motion style transfer in the experiment using the state-of-the-art method, and the results show the proposed dataset's validity for the motion style transfer task.

Cited by

• HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation
• PRISM: Polynomial Representations for Interaction-Structured Motor Control
• PhysHMR: Learning Humanoid Control Policies from Vision for Physically Plausible Human Motion Reconstruction
• MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset
• PersonaAnimator: Personalized Motion Transfer from Unconstrained Videos
• Style Transfer: A Decade Survey

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• AStF: Motion Style Transfer via Adaptive Statistics Fusor
• PersonaAnimator: Personalized Motion Transfer from Unconstrained Videos
• 3D Human Shape Style Transfer
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