.png){kind=icon}
.png){kind=icon}
Return
Multitask Non-Autoregressive Model for Human Motion Prediction
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2020.3038362.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on the previously generated ones, these models are prone to bringing issues such as error accumulation problem. In this paper, we argue that such issue is mainly caused by adopting autoregressive manner. Hence, a novel Non-AuToregressive model (NAT) is proposed with a complete non-autoregressive decoding scheme, as well as a context encoder and a positional encoding module. More specifically, the context encoder embeds the given poses from temporal and spatial perspectives. The frame decoder is responsible for predicting each future pose independently. The positional encoding module injects positional signal into the model to indicate the temporal order. Besides, a multitask training paradigm is presented for both low-level human skeleton prediction and high-level human action recognition, resulting in the considerable improvement for the prediction task. Our approach is evaluated on Human3.6M and CMU-Mocap benchmarks and outperforms state-of-the-art autoregressive methods.
Keywords:
Skeleton
Decoding
Predictive models
Task analysis
Image color analysis
Image coding
Discrete cosine transforms
Human motion prediction
non-autoregressive model
multitask learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
View more organizations