Shape and Pose Estimation for Closely Interacting Persons Using Multi‐view Images
Shape and Pose Estimation for Closely Interacting Persons Using Multi‐view Images
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DOI：
10.1111/cgf.13574
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发表时间：
2018-10
影响因子：
2.5
通讯作者：
Kun Li;Nianhong Jiao;Yebin Liu;Yangang Wang;Jingyu Yang
中科院分区：
文献类型：
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作者：
Kun Li;Nianhong Jiao;Yebin Liu;Yangang Wang;Jingyu Yang
多人姿势和形状估计非常具有挑战性，尤其是当人之间有密切的互动时。现有的方法只有在人们在捕获的图像中间隔良好时才能很好地工作。然而，在真实的生活中，人与人之间的近距离交互是非常普遍的，由于复杂的发音、频繁的遮挡和固有的歧义性，这是更大的挑战。我们提出了一种全自动无标记运动捕捉方法，可以从多视图序列中同时估计密切交互的人的3D姿势和形状。我们首先预测图像中每个人的2D关节，然后设计一个时空跟踪器，用于基于多视图视频的多人姿态跟踪。最后，我们使用蒙皮多人线性模型（SMPL）估计具有多视图约束的所有人的3D姿态和形状。实验结果表明，我们的方法实现了快速，但准确的姿态和形状估计结果的多人密切互动的情况下。与现有方法相比，该方法不需要对每个人进行预分割和人工干预，大大降低了系统的复杂度，包括时间复杂度和系统处理复杂度。
Multi‐person pose and shape estimation is very challenging, especially when the persons have close interactions. Existing methods only work well when people are well spaced out in the captured images. However, close interaction among people is very common in real life, which is more challenge due to complex articulation, frequent occlusion and inherent ambiguities. We present a fully‐automatic markerless motion capture method to simultaneously estimate 3D poses and shapes of closely interacting people from multi‐view sequences. We first predict the 2D joints for each person in an image, and then design a spatio‐temporal tracker for multi‐person pose tracking based on multi‐view videos. Finally, we estimate 3D poses and shapes of all the persons with multi‐view constraints using a skinned multi‐person linear model (SMPL). Experimental results demonstrate that our method achieves fast but accurate pose and shape estimation results for multi‐person close interaction cases. Compared with existing methods, our method does not need pre‐segmentation for each person and manual intervention, which greatly reduces the complexity of the system including time complexity and system processing complexity.