TY - JOUR
T1 - GoMAvatar
T2 - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
AU - Wen, Jing
AU - Zhao, Xiaoming
AU - Ren, Zhongzheng
AU - Schwing, Alexander G.
AU - Wang, Shenlong
N1 - Project supported by Intel AI SRS gift, IBM IIDAI Grant, Insper-Illinois Innovation Grant, NCSA Faculty Fellowship, NSF Awards #2008387, #2045586, #2106825, #2331878, #2340254, #2312102, and NIFA award 2020- 67021-32799. We thank NCSA for providing computing resources. We thank Yiming Zuo for helpful discussions.
PY - 2024
Y1 - 2024
N2 - We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel view-points, while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh (GoM) representation, a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap, PeopleSnapshot, and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).
AB - We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel view-points, while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh (GoM) representation, a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap, PeopleSnapshot, and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).
UR - http://www.scopus.com/inward/record.url?scp=85203092708&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85203092708&partnerID=8YFLogxK
U2 - 10.1109/CVPR52733.2024.00201
DO - 10.1109/CVPR52733.2024.00201
M3 - Conference article
AN - SCOPUS:85203092708
SN - 1063-6919
SP - 2059
EP - 2069
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Y2 - 16 June 2024 through 22 June 2024
ER -