ARSynth : Robust Real-Time Human Torso Tracking from Synthetically Trained Deep Neural Networks - 19.043

P. Chandran et al., "ARSynth : Robust Real-Time Human Torso Tracking from Synthetically Trained Deep Neural Networks", in Proc. of 3DBODY.TECH 2019 - 10th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Lugano, Switzerland, 22-23 Oct. 2019, pp. 43-48, doi:10.15221/19.043.

Title:

ARSynth : Robust Real-Time Human Torso Tracking from Synthetically Trained Deep Neural Networks

Authors:

Prashanth CHANDRAN 1, Endri DIBRA 2, Ben HUBER 3

1 Computer Graphics Lab, ETH Zurich, Zurich, Switzerland;
2 Arbrea Labs AG, Zurich, Switzerland

Abstract:

Robust real-time tracking of the human body is crucial to applications that benefit from live visualizations performed on the underlying body. Such applications could fall in the category of Augmented Reality for Human Bodies, finding great usage in the broader fields of Medicine and Apparel. Specifically, robust real time tracking of the female torso is a crucial component in the pre-visualization of cosmetic breast surgeries. In order to track a torso from monocular RGB input, landmarks that describe the pose and shape of the torso have to be detected. Existing state of the art in algorithms for human pose estimation are dominated by deep neural networks and rely on the availability of large databases with high quality annotations. However, for the requirement of pre-visualizing cosmetic breast surgeries, existing databases fall short as they contain no or very few landmarks that can reliably help estimate the shape of the female torso. Therefore, by building on top of openly available databases of human character models, we create a pipeline for generating synthetic female torsos in both naked and clothed scenarios. We show that deep landmark detectors trained using such a synthetic dataset are capable of generalizing well to unconstrained real world images.

Details:

Full paper: 19043chandran.pdf
Proceedings: 3DBODY.TECH 2019, 22-23 Oct. 2019, Lugano, Switzerland
Pages: 43-48
DOI: 10.15221/19.043

License/Copyright notice:

Proceedings: © Hometrica Consulting - Dr. Nicola D'Apuzzo, Switzerland, hometrica.ch.
Authors retain all rights to individual papers, which are licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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