Action-conditioned contrastive learning for 3D human pose and shape estimation in videos

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Inpyo Song , Moonwook Ryu , Jangwon Lee
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引用次数: 0

Abstract

The aim of this research is to estimate 3D human pose and shape in videos, which is a challenging task due to the complex nature of the human body and the wide range of possible pose and shape variations. This problem also poses difficulty in finding a satisfactory solution due to the trade-off between the accuracy and temporal consistency of the estimated 3D pose and shape. Thus previous researches have prioritized one objective over the other. In contrast, we propose a novel approach called the action-conditioned mesh recovery (ACMR) model, which improves accuracy without compromising temporal consistency by leveraging human action information. Our ACMR model outperforms existing methods that prioritize temporal consistency in terms of accuracy, while also achieving comparable temporal consistency with other state-of-the-art methods. Significantly, the action-conditioned learning process occurs only during training, requiring no additional resources at inference time, thereby enhancing performance without increasing computational demands.
视频中三维人体姿态和形状估计的动作条件对比学习
这项研究的目的是估计视频中的三维人体姿态和形状,由于人体的复杂性以及可能出现的各种姿态和形状变化,这是一项具有挑战性的任务。由于需要在估计的三维姿势和形状的准确性和时间一致性之间进行权衡,因此很难找到令人满意的解决方案。因此,以往的研究都是优先考虑其中一个目标。与此相反,我们提出了一种名为 "动作条件网格恢复(ACMR)"模型的新方法,通过利用人体动作信息,在不影响时间一致性的前提下提高了准确性。我们的 ACMR 模型在准确性方面优于优先考虑时间一致性的现有方法,同时在时间一致性方面也达到了其他最先进方法的水平。值得注意的是,以动作为条件的学习过程只发生在训练过程中,在推理时不需要额外资源,从而在不增加计算需求的情况下提高了性能。
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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