arXiv Open Access 2023

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

Makito Kobayashi Chen-Chieh Liao Keito Inoue Sentaro Yojima Masafumi Takahashi

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Abstrak

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.

Topik & Kata Kunci

cs.CV cs.AI

Penulis (5)

Makito Kobayashi

Chen-Chieh Liao

Keito Inoue

Sentaro Yojima

Masafumi Takahashi

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Kobayashi, M., Liao, C., Inoue, K., Yojima, S., Takahashi, M. (2023). Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization. https://arxiv.org/abs/2306.08861

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓