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2022

Denoising Diffusion Probabilistic Models for Styled Walking Synthesis

Findlay, Edmund J. C., Zhang, Haozheng, Chang, Ziyi et al.

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Generating realistic motions for digital humans is time-consuming for many graphics applications.

Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models.
These results offer high-quality motions but typically suffer in motion style diversity.
For the first time, we propose a framework using the denoising diffusion probabilistic model (DDPM) to synthesize styled human motions, integrating two tasks into one pipeline with increased style diversity compared with traditional motion synthesis methods.

Denoising Diffusion Probabilistic Models for Styled Walking Synthesis · Around

Built on

Realtime Style Transfer for Unlabeled Heterogeneous Human Motion
Shihong Xia, Congyi Wang, Jinxiang Chai, and Jessica Hodgins. 2015 · 2015
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A Deep Learning Framework for Character Motion Synthesis and Editing
Daniel Holden, Jun Saito, and Taku Komura. 2016 · 2016
Earlier work this paper cites.
Spectral Style Transfer for Human Motion between Independent Actions
M. Ersin Yumer and Niloy J. Mitra. 2016 · 2016
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Then

Action2Motion: Conditioned Generation of 3D Human Motions. In Proceedings of the 28th ACM International Conference on Multimedia (MM ’20)
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Denoising Diffusion Probabilistic Models
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Later among the works it cites.

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