基于单-多视图优化的足球球员三维姿态和体型估计

doi:10.19678/j.issn.1000-3428.0067480

摘要/Abstract

摘要：

足球比赛场景的三维重建有助于观众自由切换视角，增加了互动性和沉浸感。针对足球比赛场景中的足球球员，提出一种三维姿态和体型估计方法。对球员的多视图图像使用训练好的部分注意力回归的三维人体估计(PARE)模型生成初始的三维姿态和体型估计，并使用人工标注的二维关节点作为优化目标。单-多视图优化操作利用蒙皮多人线性模型(SMPL)和正交投影的可微性，将球员的三维姿态和体型参数映射到二维关节点，计算其与人工标注之间的差异，再使用神经网络的反向传播算法更新三维姿态和体型参数，持续这些过程直到差异最小化。在自建的足球球员多视图数据集上的实验结果表明，该方法能够有效估计足球球员的三维姿态和体型，与人体网格恢复、在循环中优化SMPL、PARE等方法相比，二维关节点精度在单视图上提高了9.2%~37.5%，在多视图交叉验证中提高了34.9%~54.1%。

关键词: 三维姿态和体型估计, 参数化人体模型, 单-多视图优化, 反向传播, 蒙皮多人线性模型

Abstract:

Reconstructing a Three-Dimensional(3D) scene of a soccer game allows the audience to switch their viewpoints freely, enhancing interactivity and immersion. A pipeline to estimate the 3D pose and shape of soccer players in a soccer scene is proposed. The proposed method obtains an initial 3D pose and shape estimation of soccer players with a trained Part Attention Regressor for a 3D human body Estimation(PARE) model, and utilizes annotated Two-Dimensional(2D) joints of the human body as the optimization target. Single- and multi-view optimization operations utilize the differentiability of a Skinned Multi-Person Linear(SMPL) model and orthogonal projection, map the 3D pose and shape parameters to predicted 2D joints, obtain the difference with annotations, and subsequently update these parameters with a backpropagation algorithm of neural networks. These processes continue until the difference is minimized. Experimental results on our soccer players multi-view dataset demonstrate that the proposed method is able to estimate the 3D pose and body shape of soccer players. Compared with Human Mesh Recovery(HMR), SMPL oPtimization IN the loop(SPIN), and PARE methods, the accuracy of 2D joints improved by 9.2%-37.5% on single-view, and by 34.9%-54.1% on multi-view cross validation.

Key words: Three-Dimensional(3D) pose and body shape estimation, parametric human body model, single- and multi-view optimization, backpropagation, Skinned Multi-Person Linear(SMPL) model

谢欢, 刘纯平, 季怡. 基于单-多视图优化的足球球员三维姿态和体型估计[J]. 计算机工程, 2024, 50(3): 200-207.

Huan XIE, Chunping LIU, Yi JI. Three-Dimensional Pose and Body Shape Estimation of Soccer Players Based on Single- and Multi-View Optimization[J]. Computer Engineering, 2024, 50(3): 200-207.

https://www.ecice06.com/CN/Y2024/V50/I3/200

图/表 10

图1 足球球员三维姿态和体型估计总体框架

Fig.1 Overall framework of 3D pose and body shape estimation of soccer players

图2 人体二维关节点示例

Fig.2 Example of human body 2D joint points

图3 足球游戏《FIFA 21》中的不同视图

Fig.3 Different views in the soccer game FIFA 21

图4 二维关节点检测结果

Fig.4 2D joint point detection results

图5 三维姿态和体型的初始估计误差

Fig.5 Initial estimation error of 3D pose and body shape

图6 单视图优化结果应用到其他视图上的差异比较

Fig.6 Comparison of differences by applying single-view optimization results to other views

图7 所提方法与其他方法的可视化结果比较

Fig.7 Comparison of visualization results of the proposed method with other methods

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