Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling
CVPR 2024(2023)
摘要
Modeling animatable human avatars from RGB videos is a long-standing and
challenging problem. Recent works usually adopt MLP-based neural radiance
fields (NeRF) to represent 3D humans, but it remains difficult for pure MLPs to
regress pose-dependent garment details. To this end, we introduce Animatable
Gaussians, a new avatar representation that leverages powerful 2D CNNs and 3D
Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians
with the animatable avatar, we learn a parametric template from the input
videos, and then parameterize the template on two front & back canonical
Gaussian maps where each pixel represents a 3D Gaussian. The learned template
is adaptive to the wearing garments for modeling looser clothes like dresses.
Such template-guided 2D parameterization enables us to employ a powerful
StyleGAN-based CNN to learn the pose-dependent Gaussian maps for modeling
detailed dynamic appearances. Furthermore, we introduce a pose projection
strategy for better generalization given novel poses. Overall, our method can
create lifelike avatars with dynamic, realistic and generalized appearances.
Experiments show that our method outperforms other state-of-the-art approaches.
Code: https://github.com/lizhe00/AnimatableGaussians
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