Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic
  Gaussians
Creating high-fidelity 3D head avatars has always been a research hotspot,
but there remains a great challenge under lightweight sparse view setups. In
this paper, we propose Gaussian Head Avatar represented by controllable 3D
Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D
Gaussians and a fully learned MLP-based deformation field to capture complex
expressions. The two parts benefit each other, thereby our method can model
fine-grained dynamic details while ensuring expression accuracy. Furthermore,
we devise a well-designed geometry-guided initialization strategy based on
implicit SDF and Deep Marching Tetrahedra for the stability and convergence of
the training procedure. Experiments show our approach outperforms other
state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering
quality at 2K resolution even under exaggerated expressions.
65713d8f974502490bc6a14c
Yuelang Xu
65713d8f974502490bc6a14d
Benwang Chen
admin_assigned
2023-12-07T11:43:00.131Z
63e5efafb6a40bf941da1806
/avatars/1a73a63bb3fda6dfe326ced338d747e4.svg
cc
user
ben55
65713d8f974502490bc6a14e
Zhe Li
65713d8f974502490bc6a14f
Hongwen Zhang
admin_assigned
2023-12-07T11:43:43.648Z
638715a1aaee5cd69dc0c401
/avatars/f5976b3b02fe86191cb7f59c3dae102b.svg
Hongwen Zhang
user
HongwenZhang
65713d8f974502490bc6a150
Lizhen Wang
admin_assigned
2023-12-07T11:43:36.947Z
63451d2dfeba4bdba59cf9b1
/avatars/ff5dd2f3b502c54e7c3e2512c3e98b28.svg
Lizhen Wang
user
wanglz14
65713d8f974502490bc6a151
Zerong Zheng
65713d8f974502490bc6a152
Yebin Liu
admin_assigned
2023-12-07T11:43:22.316Z
62e14dbe4db2175cd2735a80
/avatars/e6385dcedcb97e1b36281b49210321aa.svg
Yebin Liu
user
YebinLiu
Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic
  Gaussians
Creating high-fidelity 3D head avatars has always been a research hotspot,
but there remains a great challenge under lightweight sparse view setups. In
this paper, we propose Gaussian Head Avatar represented by controllable 3D
Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D
Gaussians and a fully learned MLP-based deformation field to capture complex
expressions. The two parts benefit each other, thereby our method can model
fine-grained dynamic details while ensuring expression accuracy. Furthermore,
we devise a well-designed geometry-guided initialization strategy based on
implicit SDF and Deep Marching Tetrahedra for the stability and convergence of
the training procedure. Experiments show our approach outperforms other
state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering
quality at 2K resolution even under exaggerated expressions.
The method uses controllable 3D Gaussians and a fully learned MLP-based deformation field for high-fidelity 3D head avatar modeling under sparse views, employing geometry-guided initialization with implicit SDF and Deep Marching Tetrahedra for stability and high rendering quality.
3D Gaussians MLP-based deformation field implicit SDF Deep Marching Tetrahedra high-fidelity 3D head avatars sparse views ultra high-fidelity rendering