MVCHead uses a hierarchical state space model with bi-directional scans and an SE(3) critic to enforce 3D consistency in Gaussian avatars trained only on 2D images.
Panolam: Large avatar model for gaussian full- head synthesis from one-shot unposed image
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MeshLAM reconstructs high-fidelity animatable textured mesh head avatars from a single image via a feed-forward dual shape-texture architecture with iterative GRU decoding and reprojection-based guidance.
HeadsUp reconstructs high-quality 3D Gaussian heads from multi-view images via an encoder-decoder outputting UV-parameterized Gaussians on a neutral template, trained on over 10,000 subjects for generalization and downstream tasks.
citing papers explorer
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Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation
MVCHead uses a hierarchical state space model with bi-directional scans and an SE(3) critic to enforce 3D consistency in Gaussian avatars trained only on 2D images.
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MeshLAM: Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction
MeshLAM reconstructs high-fidelity animatable textured mesh head avatars from a single image via a feed-forward dual shape-texture architecture with iterative GRU decoding and reprojection-based guidance.
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Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures
HeadsUp reconstructs high-quality 3D Gaussian heads from multi-view images via an encoder-decoder outputting UV-parameterized Gaussians on a neutral template, trained on over 10,000 subjects for generalization and downstream tasks.
- Any3DAvatar: Fast and High-Quality Full-Head 3D Avatar Reconstruction from Single Portrait Image