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- W4297818833 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." @default.
- W4297818833 created "2022-10-01" @default.
- W4297818833 creator A5005626854 @default.
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- W4297818833 date "2020-12-29" @default.
- W4297818833 modified "2023-10-16" @default.
- W4297818833 title "Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory" @default.
- W4297818833 doi "https://doi.org/10.48550/arxiv.2012.14739" @default.
- W4297818833 hasPublicationYear "2020" @default.
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