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基于实时视频流的3D人体姿势和形状估计OACSTPCD

3D Human Pose and Shape Estimation Based on Live Video Streams

中文摘要英文摘要

为满足元宇宙、游戏及虚拟现实等应用场景中对实时视频流3D人体姿势和形状估计准确性和真实性的要求,提出了一种基于时间注意力机制的3D人体姿势和形状估计方法.首先,提取图像特征,并将其输入运动连续注意力模块以更好地校准需要注意的时间序列范围;随后,使用实时特征注意力集成模块以有效地组合当前帧与过去帧的特征表示;最后,通过人体参数回归网络得到最终结果,并使用基于图卷积的生成对抗网络判断模型是否来自真实的人体运动数据.相较于之前基于实时视频流的方法,在主流数据集上加速度误差平均减少了 30%的同时,网络参数与计算量减少了 65%,在实际测试中实现了每秒55-60帧的3D人体姿态和形状估计速度,为元宇宙、游戏及虚拟现实等应用场景提供更好的用户体验和更高的应用价值.

A 3D human body pose and shape estimation method based on a temporal attention mechanism is proposed to meet the requirements of real-time accuracy and realism in 3D human body pose and shape estimation for applications such as the metaverse,gaming,and virtual reality.First,image features are extracted and input into a motion continuity attention module to better calibrate the time sequence range that requires attention.Then,a real-time feature attention integration module is used to effectively combine the feature representations of the current frame and past frames.Finally,the human parameter regression network is used to obtain the final results,and a graph convolutional generative adversarial network is used to determine whether the model comes from real human motion data.Compared with previous methods based on real-time video streams,the proposed method reduces the acceleration error by an average of 30%on mainstream datasets,while reducing the network parameters and computational complexity by 65%.The proposed method achieves a 3D human body pose and shape estimation speed of 55~60 frames per second in practical tests,providing better user experience and higher application value for applications such as the metaverse,gaming,and virtual reality.

朱越;黄海于;罗学义

西南交通大学计算机与人工智能学院,四川成都 611756消防救援局昆明训练总队,云南昆明 650217

计算机与自动化

三维人体重建SMPL模型实时特征注意力集成图卷积神经网络机器学习

3 D human reconstructionskinned multi-person linear modelreal-time feature attention integrationgraph convolutional neural networkmachine learning

《计算机技术与发展》 2024 (004)

42-47 / 6

应急管理部消防救援局科技创新项目(2020XFCX29)

10.20165/j.cnki.ISSN1673-629X.2024.0007

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