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GPAvatar: Generalizable and Precise Head Avatar from Image(s)
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GPAvatar: Generalizable and Precise Head Avatar from Image(s)
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Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision community. The fundamental objective of this field is to faithfully recreate the head avatar and precisely control expressions and postures. Existing methods, categorized into 2D-based warping, mesh-based, and neural rendering approaches, present challenges in maintaining multi-view consistency, incorporating non-facial information, and generalizing to new identities. In this paper, we propose a framework named GPAvatar that reconstructs 3D head avatars from one or several images in a single forward pass. The key idea of this work is to introduce a dynamic point-based expression field driven by a point cloud to precisely and effectively capture expressions. Furthermore, we use a Multi Tri-planes Attention (MTA) fusion module in the tri-planes canonical field to leverage information from multiple input images. The proposed method achieves faithful identity reconstruction, precise expression control, and multi-view consistency, demonstrating promising results for free-viewpoint rendering and novel view synthesis.
Forward citations
Cited by 14 Pith papers
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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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Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures
HeadsUp maps multi-view captures to UV-parameterized 3D Gaussians on a template via an encoder-decoder, achieving state-of-the-art quality and generalization after training on more than 10,000 subjects.
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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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TongueReenact: Geometry-Anchored Tongue Synthesis for Face Reenactment
A two-stage pipeline (Gaussian reenactment plus geometry-anchored masked diffusion) transfers cross-identity tongue dynamics, roughly doubling tongue-specific metrics over prior reenactment baselines.
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FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images
FFAvatar uses a Transformer-based 3D Gaussian model with alternating attention and sparse-to-dense learning to enable feed-forward, incremental reconstruction of animatable 4D head avatars from sparse portrait images.
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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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S-Avatar: Diffusion-Guided Gaussian Head Avatars from a Single Image
A three-stage pipeline generates animatable 3D Gaussian head avatars from one image by diffusion-based splat synthesis, FLAME fitting, and inverse-distance binding with scale adaptation.
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Split and Drive: Dual-Axis Disentanglement for Real-Time Gaussian Head Avatars
A single-image 3DGS head avatar with internalized motion encoding and three region-specialized Gaussian branches runs real-time end-to-end and matches or beats recent baselines on reenactment metrics.
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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 dow...
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Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures
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