SplatShot is a training-free method that inserts per-step 3DGS refitting and photometric feedback into diffusion denoising to enforce multi-view consistency for single-photo 3D face avatars.
Gpavatar: Generaliz- able and precise head avatar from image (s)
8 Pith papers cite this work. Polarity classification is still indexing.
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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.
AvatarPointillist autoregressively generates adaptive 3D point clouds via Transformer for photorealistic 4D Gaussian avatars from one image, jointly predicting animation bindings and using a conditioned Gaussian decoder.
UIKA is a feed-forward animatable Gaussian head model using UV-guided correspondence estimation and learnable UV tokens with dual-level attention, trained on large-scale synthetic data to handle pose-free inputs.
A Transformer 3D-Gaussian model reconstructs incremental, animatable 4D head avatars from sparse portraits via alternating attention, sparse-to-dense UV densification, and residual motion refinement.
SAGE self-learns Gaussian expression deformations via joint surfel-SDF optimization and self-supervised consistency, enabling comparable avatar quality from single frames, monocular rotations, or one-shot inputs.
FlexAvatar introduces bias sinks in a transformer to unify monocular and multi-view training, yielding complete 3D head avatars with strong generalization and view extrapolation from single images.
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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Splatshot: 3D Face Avatar Generation from a Single Unconstrained Photo
SplatShot is a training-free method that inserts per-step 3DGS refitting and photometric feedback into diffusion denoising to enforce multi-view consistency for single-photo 3D face avatars.
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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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AvatarPointillist: AutoRegressive 4D Gaussian Avatarization
AvatarPointillist autoregressively generates adaptive 3D point clouds via Transformer for photorealistic 4D Gaussian avatars from one image, jointly predicting animation bindings and using a conditioned Gaussian decoder.
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UIKA: Fast Universal Head Avatar from Pose-Free Images
UIKA is a feed-forward animatable Gaussian head model using UV-guided correspondence estimation and learnable UV tokens with dual-level attention, trained on large-scale synthetic data to handle pose-free inputs.
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FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images
A Transformer 3D-Gaussian model reconstructs incremental, animatable 4D head avatars from sparse portraits via alternating attention, sparse-to-dense UV densification, and residual motion refinement.
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Self-Learning Expression Deformations for Data-Efficient Gaussian Avatars
SAGE self-learns Gaussian expression deformations via joint surfel-SDF optimization and self-supervised consistency, enabling comparable avatar quality from single frames, monocular rotations, or one-shot inputs.
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FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision
FlexAvatar introduces bias sinks in a transformer to unify monocular and multi-view training, yielding complete 3D head avatars with strong generalization and view extrapolation from single images.
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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.