@inproceedings{13a3424d39d148fd9586afecca80a384,
title = "Single-image mesh reconstruction and pose estimation via generative normal map",
abstract = "We present a unified learning framework for recovering both 3D mesh and camera pose of the object from a single image. Our approach learns to recover outer shape and surface geometric details of the mesh without relying on 3D supervision. We adopt multi-view normal maps as the 2D supervision so that the silhouette and geometric details information can be transferred to neural network. A normal mismatch based objective function is introduced to train the network, and the camera pose is parameterized into the objective, it integrates pose estimation with the mesh reconstruction in a same optimization procedure. We demonstrate the abilities of the proposed approach in generating 3D mesh and estimating camera pose with qualitative and quantitative experiments.",
keywords = "Deep learning, Mesh reconstruction, Pose estimation",
author = "Nan Xiang and Li Wang and Tao Jiang and Yanran Li and Xiaosong Yang and Jianjun Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 32nd International Conference on Computer Animation and Social Agents, CASA 2019 ; Conference date: 01-07-2019 Through 03-07-2019",
year = "2019",
month = jul,
day = "1",
doi = "10.1145/3328756.3328766",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "79--84",
booktitle = "Proceedings of the 32nd International Conference on Computer Animation and Social Agents, CASA 2019",
}