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Generalizable and Animatable Gaussian Head Avatar
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In this paper, we propose Generalizable and Animatable Gaussian head Avatar (GAGAvatar) for one-shot animatable head avatar reconstruction. Existing methods rely on neural radiance fields, leading to heavy rendering consumption and low reenactment speeds. To address these limitations, we generate the parameters of 3D Gaussians from a single image in a single forward pass. The key innovation of our work is the proposed dual-lifting method, which produces high-fidelity 3D Gaussians that capture identity and facial details. Additionally, we leverage global image features and the 3D morphable model to construct 3D Gaussians for controlling expressions. After training, our model can reconstruct unseen identities without specific optimizations and perform reenactment rendering at real-time speeds. Experiments show that our method exhibits superior performance compared to previous methods in terms of reconstruction quality and expression accuracy. We believe our method can establish new benchmarks for future research and advance applications of digital avatars. Code and demos are available https://github.com/xg-chu/GAGAvatar.
Forward citations
Cited by 7 Pith papers
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Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation
CLIPortrait disentangles camera and geometry information from CLIP embeddings via 2D canonicalization, then prevents distribution collapse with a Jacobian regularizer, enabling text-guided 3D portrait generation from ...
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Total-Editing: Head Avatar with Editable Appearance, Motion, and Lighting
Total-Editing is a unified 3D head avatar framework that separately controls appearance, motion, and lighting through an intrinsically decomposed neural radiance field, and reports stronger identity, expression, pose,...
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FaceCraft4D: Animated 3D Facial Avatar Generation from a Single Image
FaceCraft4D generates a full-360-degree, FLAME-animatable 3D Gaussian head avatar from a single image by synthesizing multiview videos with shape, image, and video priors and optimizing them with COIN training.
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3D Gaussian Head Avatars with Expressive Dynamic Appearances by Compact Tensorial Representations
A 3D Gaussian head avatar method uses triplanes for neutral appearance and per-blendshape 1D feature lines for dynamic opacity offsets, reaching 10MB storage and 300 FPS with competitive fidelity.
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GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion
A normal-map-conditioned multi-view head diffusion model generates pseudo-ground-truth views that regularize Gaussian avatar optimization, improving reconstruction of unobserved head regions from monocular videos.
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GGTalker: Talking Head Systhesis with Generalizable Gaussian Priors and Identity-Specific Adaptation
GGTalker combines large-scale audio-to-expression and expression-to-texture priors with rapid per-identity fine-tuning to create high-quality 3D talking heads from a short video.
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Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects
3D Gaussian Splatting research relevant to Extended Reality is organized into a five-part taxonomy with suggested future directions.
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