Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory

• URL: http://arxiv.org/abs/2012.14739v1
• Date: Tue, 29 Dec 2020 12:57:22 GMT
• Title: Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory
• Authors: Yu Rong, Ziwei Liu, Chen Change Loy
• Abstract summary: We propose a prototype memory-augmented network, PM-Net, that effectively improves performances of predicting rare poses. In this work, we 1) identify and analyze this learning obstacle and 2) propose a prototype memory-augmented network, PM-Net, that effectively improves performances of predicting rare poses.
• Score: 98.36233875637168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have achieved great progress in single-image 3D human reconstruction. However, existing methods still fall short in predicting rare poses. The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare poses. In this work, we 1) identify and analyze this learning obstacle and 2) propose a prototype memory-augmented network, PM-Net, that effectively improves performances of predicting rare poses. The core of our framework is a memory module that learns and stores a set of 3D human prototypes capturing local distributions for either common poses or rare poses. With this formulation, the regression starts from a better initialization, which is relatively easier to converge. Extensive experiments on several widely employed datasets demonstrate the proposed framework's effectiveness compared to other state-of-the-art methods. Notably, our approach significantly improves the models' performances on rare poses while generating comparable results on other samples.

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