Monocular Total Capture: Posing Face, Body, and Hands in the Wild
Donglai Xiang, Hanbyul Joo, Yaser Sheikh

TL;DR
This paper introduces a novel monocular method for capturing full 3D human motion, including face, body, and hands, using a new dataset and a 3D Part Orientation Fields representation.
Contribution
It presents the first approach to reconstruct complete 3D total body motion from monocular images using POFs and a deformable human model, along with a new dataset.
Findings
Accurate 3D total body motion reconstruction from monocular input.
Effective use of 3D Part Orientation Fields for pose estimation.
Robust performance on challenging in-the-wild videos.
Abstract
We present the first method to capture the 3D total motion of a target person from a monocular view input. Given an image or a monocular video, our method reconstructs the motion from body, face, and fingers represented by a 3D deformable mesh model. We use an efficient representation called 3D Part Orientation Fields (POFs), to encode the 3D orientations of all body parts in the common 2D image space. POFs are predicted by a Fully Convolutional Network (FCN), along with the joint confidence maps. To train our network, we collect a new 3D human motion dataset capturing diverse total body motion of 40 subjects in a multiview system. We leverage a 3D deformable human model to reconstruct total body pose from the CNN outputs by exploiting the pose and shape prior in the model. We also present a texture-based tracking method to obtain temporally coherent motion capture output. We perform…
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Taxonomy
TopicsHuman Pose and Action Recognition · Advanced Vision and Imaging · Human Motion and Animation
