Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162631
Title: Investigating pose representations and motion contexts modeling for 3D motion prediction
Authors: Liu, Zhenguang
Wu, Shuang
Jin, Shuyuan
Liu, Qi
Ji, Shouling
Lu, Shijian
Cheng, Li
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Liu, Z., Wu, S., Jin, S., Liu, Q., Ji, S., Lu, S. & Cheng, L. (2022). Investigating pose representations and motion contexts modeling for 3D motion prediction. IEEE Transactions On Pattern Analysis and Machine Intelligence, 3139918-. https://dx.doi.org/10.1109/TPAMI.2021.3139918
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Abstract: Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results. Yet there is no effort that investigates across different pose representation schemes. We conduct an indepth study on various pose representations with a focus on their effects on the motion prediction task. Moreover, recent approaches build upon off-the-shelf RNN units for motion prediction. These approaches process input pose sequence sequentially and inherently have difficulties in capturing long-term dependencies. In this paper, we propose a novel RNN architecture termed AHMR for motion prediction which simultaneously models local motion contexts and a global context. We further explore a geodesic loss and a forward kinematics loss, which have more geometric significance than the widely employed L2 loss. Interestingly, we applied our method to a range of articulate objects including human, fish, and mouse. Empirical results show that our approach outperforms the state-of-the-art methods in short-term prediction and achieves much enhanced long-term prediction proficiency, such as retaining natural human-like motions over 50 seconds predictions. Our codes are released.
URI: https://hdl.handle.net/10356/162631
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2021.3139918
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

SCOPUSTM   
Citations 20

21
Updated on Nov 26, 2022

Web of ScienceTM
Citations 10

27
Updated on Dec 2, 2022

Page view(s)

8
Updated on Dec 2, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.