Denoising Diffusion Probabilistic Models for Action-Conditioned 3D Motion Generation
Author:
Affiliation:
1. Beihang University
2. Peking University, Shenzhen Graduate School,National Key Laboratory of General Artificial Intelligence
3. University of Pisa
4. University of Trento
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446185.pdf?arnumber=10446185
Reference34 articles.
1. Neural animation layering for synthesizing martial arts movements
2. TEMOS: Generating Diverse Human Motions from Textual Descriptions
3. Skeleton-Based Action Recognition With Focusing-Diffusion Graph Convolutional Networks
4. MTT: Multi-Scale Temporal Transformer for Skeleton-Based Action Recognition
5. Bayesian Adversarial Human Motion Synthesis
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