@inproceedings{3962491b401542338f2b8980a3eaad1a,
title = "Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder",
abstract = "In this paper, we present a deep generative model based method to generate diverse human motion interpolation results. We resort to the Conditional Variational Auto-Encoder (CVAE) to learn human motion conditioned on a pair of given start and end motions, by leveraging the Recurrent Neural Network (RNN) structure for both the encoder and the decoder. Additionally, we introduce a regularization loss to further promote sample diversity. Once trained, our method is able to generate multiple plausible coherent motions by repetitively sampling from the learned latent space. Experiments on the publicly available dataset demonstrate the effectiveness of our method, in terms of sample plausibility and diversity.",
keywords = "Conditional Variational Auto-Encoder, Diverse human motion interpolation",
author = "Chunzhi Gu and Shuofeng Zhao and Chao Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 ; Conference date: 04-01-2022 Through 06-01-2022",
year = "2022",
doi = "10.1117/12.2625851",
language = "英語",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masayuki Nakajima and Shogo Muramatsu and Jae-Gon Kim and Jing-Ming Guo and Qian Kemao",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2022",
}