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Animatable and Relightable Gaussians for High-fidelity Human Avatar Modeling
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Animatable and Relightable Gaussians for High-fidelity Human Avatar Modeling
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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. To tackle the realistic relighting of animatable avatars, we introduce physically-based rendering into the avatar representation for decomposing avatar materials and environment illumination. Overall, our method can create lifelike avatars with dynamic, realistic, generalized and relightable appearances. Experiments show that our method outperforms other state-of-the-art approaches.
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Cited by 7 Pith papers
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PiG-Avatar decouples Gaussian avatar geometry from body-template surfaces by anchoring points in a neural-field volumetric canonical space and transporting them with 3D barycentric coordinates.
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PiG-Avatar: Hierarchical Neural-Field-Guided Gaussian Avatars
PiG-Avatar decouples Gaussian avatar geometry from body-template surfaces by anchoring Gaussians in a neural-field-governed volumetric canonical space and using barycentric transport for kinematics, yielding SOTA rend...
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A conditioning-guided constrained inversion method restricts avatar edits to a low-dimensional part-specific subspace and uses an information matrix spectrum from pipeline linearization to predict and ensure stability...
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HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis
HumanOLAT is the first public full-body OLAT dataset: 21 subjects, 3 poses, 40 views, 331 single-light captures, plus environment maps, color gradients, meshes and normals.
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AHOY! Animatable Humans under Occlusion from YouTube Videos with Gaussian Splatting and Video Diffusion Priors
Identity-finetuned video diffusion plus RF-Inversion can supply multi-view body supervision that lets 3D Gaussian avatars be completed and animated from heavily occluded monocular video.
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GaussianGAN: Real-Time Photorealistic controllable Human Avatars
GaussianGAN generates photorealistic human avatars in real time by densifying Gaussian points around skeleton limbs and refining rendered features with a UNet.
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