Published at: VRCAI '19: The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry, November 2019 Article No. 12 (best paper award)
Abstract:
With the development of AR/VR technologies, a reliable and straightforward way to digitize three-dimensional human body is in high demand. Most existing methods use complex equipment and sophisticated algorithms. This is impractical for everyday users. In this paper, we propose a pipeline that reconstructs 3D human shape avatar at a glance. Our approach simultaneously reconstructs the three-dimensional human geometry and whole body texture map with only a single RGB image as input. We first segment the human body part from the image and then obtain an initial body geometry by fitting the segment to a parametric model. Next, we warp the initial geometry to the final shape by applying a silhouette-based dense correspondence. Finally, to infer invisible backside texture from a frontal image, we propose a network we call InferGAN. Comprehensive experiments demonstrate that our solution is robust and effective on both public and our own captured data. Our human avatars can be easily rigged and animated using MoCap data. We developed a mobile application that demonstrates this capability in AR/VR settings.
3D Human Avatar Digitization from a Single Image