2024
CVPR
CVPR 2024
GART: Gaussian Articulated Template Models
Abstract
We introduce Gaussian Articulated Template Model (GART) an explicit efficient and expressive representation for non-rigid articulated subject capturing and rendering from monocular videos. GART utilizes a mixture of moving 3D Gaussians to explicitly approximate a deformable subject's geometry and appearance. It takes advantage of a categorical template model prior (SMPL SMAL etc.) with learnable forward skinning while further generalizing to more complex non-rigid deformations with novel latent bones. GART can be reconstructed via differentiable rendering from monocular videos in seconds or minutes and rendered in novel poses faster than 150fps.
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Keyword Pioneer
— gaussian articulated template
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Hot Topic Early Bird
— 3d gaussian splatting
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Keywords
3d reconstruction
pose estimation
human pose estimation
3d gaussian splatting
differentiable rendering
monocular video
3d gaussian
gaussian rendering
monocular reconstruction
articulated model
deformable geometry
skinning weight
novel pose synthesis
template model
articulated body
gaussian articulated template
articulated template
articulated tracking
articulated modeling
deformable subject
skinned template model