Monocular Expressive Body Regression through Body-Driven Attention
| Autor: | Michael J. Black, Georgios Pavlakos, Timo Bolkart, Dimitrios Tzionas, Vasileios Choutas |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Monocular Artificial neural network Pixel Exploit Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Graphics (cs.GR) Computer Science - Graphics Local optimum Face (geometry) 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Computer vision Artificial intelligence Focus (optics) business |
| Zdroj: | Computer Vision – ECCV 2020 ISBN: 9783030586065 ECCV (10) Computer Vision-ECCV 2020 Lecture Notes in Computer Science |
| DOI: | 10.48550/arxiv.2008.09062 |
| Popis: | To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de . Comment: Accepted in ECCV'20. Project page: http://expose.is.tue.mpg.de |
| Databáze: | OpenAIRE |
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