Abstract
Rapid pose classification and pose retrieval in 3D human datasets are important problems in shape analysis. In this paper, we extend the Multi-View Convolutional Neural Network (MVCNN) with ordered view feature fusion for orientation-aware 3D human pose classification and retrieval. Firstly, we combine each learned view feature in an orderly manner to form a compact representation for orientation-aware pose classification. Secondly, for pose retrieval, the Siamese network is adopted to learn descriptor vectors, where their L2 distances are close for pairs of shapes with the same poses and are far away for pairs of shapes with different poses. Furthermore, we also construct a larger 3D Human Pose Recognition Dataset (HPRD) consisting of 100,000 shapes for the evaluation of pose classification and retrieval. Experiments and comparisons demonstrate that our method obtains better results than previous works of pose classification and retrieval on the 3D human datasets, such as SHREC’14, FAUST, and HPRD.
Highlights
With the rapid development of 3D scanning and capturing technologies, an increasing amount of 3D human shape datasets have emerged, such as SHREC’14 [1], the statistical model shape [2], Civilian American and European Surface Anthropometry Resource (CAESAR) [3], FAUST [4], and SURREAL [5], which usually consist of subjects in different poses.some classical parametric modeling methods have been proposed for generating human subjects and poses, such as SCAPE [6] and SMPL [7]
Nearest Neighbour (NN): the percentage of the closest matches that belongs to the same class as the query
We plot the retrieval losses of the first 100 steps on training and testing datasets of the SHREC REal (SH-RE), SH-SY, and FAUST in Figure 14, which shows that the training process converges quickly
Summary
Some classical parametric modeling methods have been proposed for generating human subjects and poses, such as SCAPE [6] and SMPL [7]. The analysis and comparisons of classical parametric modeling methods of 3D human have been reported in surveys [8,9]. Most existing recognition approaches of 3D human datasets are focusing on shape retrieval whereby a deformable subject’s shape is recognized regardless of its pose [11]. There are fewer works on 3D human pose classification and retrieval, i.e., recognizing a given pose regardless of the subject of the pose taken, such as Slama et al [12] and Bonis et al [13]. The previous shape classification and retrieval methods, such as Su et al [14] and Pickup et al [11], Electronics 2020, 9, 1368; doi:10.3390/electronics9091368 www.mdpi.com/journal/electronics
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