Abstract
Existing monocular 3D human pose and body shape (HPS) estimation methods make the coplanar assumption and use weak perspective projection in order to simplify the problem setting for images in the wild. However, weak perspective projection inevitably introduce prediction biases. To address this issue, we propose a plug-and-play Perspective Residual Log-likehood on Monocular 3D HPS Estimation (PRIME) module to significantly improve the accuracy of monocular 3D HPS estimation with trivial sacrifice on running time. PRIME applies full perspective projection to construct 2D re-projection loss or extract mesh-alignment features. Specifically, PRIME estimates the distribution of 2D joints and scale to calculate the perspective translation with the focal length. Further, we introduce side view constrain (SVC) of 2D joints to reduce the ambiguity of 3D HPS recovery. Experimental results demonstrate the effectiveness of our method.
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