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HeadGAP: Few-Shot 3D Head Avatar via Generalizable Gaussian Priors

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arxiv 2408.06019 v2 pith:V5NIXICJ submitted 2024-08-12 cs.CV

Xiaozheng Zheng , Chao Wen , Zhaohu Li , Weiyi Zhang , Zhuo Su , Xu Chang , Yang Zhao , Zheng Lv
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Xiaoyuan Zhang Yongjie Zhang Guidong Wang Lan Xu
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classification cs.CV
keywords avatarheadpriorscreationfew-shotgaussianpersonalizationphase
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In this paper, we present a novel 3D head avatar creation approach capable of generalizing from few-shot in-the-wild data with high-fidelity and animatable robustness. Given the underconstrained nature of this problem, incorporating prior knowledge is essential. Therefore, we propose a framework comprising prior learning and avatar creation phases. The prior learning phase leverages 3D head priors derived from a large-scale multi-view dynamic dataset, and the avatar creation phase applies these priors for few-shot personalization. Our approach effectively captures these priors by utilizing a Gaussian Splatting-based auto-decoder network with part-based dynamic modeling. Our method employs identity-shared encoding with personalized latent codes for individual identities to learn the attributes of Gaussian primitives. During the avatar creation phase, we achieve fast head avatar personalization by leveraging inversion and fine-tuning strategies. Extensive experiments demonstrate that our model effectively exploits head priors and successfully generalizes them to few-shot personalization, achieving photo-realistic rendering quality, multi-view consistency, and stable animation.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AvatarPointillist: AutoRegressive 4D Gaussian Avatarization

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    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.

  2. VRGaussianAvatar: Integrating 3D Gaussian Avatars into VR

    cs.CV 2026-02 conditional novelty 7.0 of 10

    VRGaussianAvatar enables real-time full-body 3D Gaussian Splatting avatars in VR from HMD tracking alone via inverse kinematics and binocular batching for efficient stereo rendering, outperforming mesh baselines in pe...

  3. UIKA: Fast Universal Head Avatar from Pose-Free Images

    cs.CV 2026-01 conditional novelty 7.0 of 10

    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.

  4. GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar

    cs.GR 2025-07 conditional novelty 7.0 of 10

    GeoAvatar improves 3D head avatar quality by adaptively regulating Gaussian offsets per facial region, adding a detailed mouth structure with part-wise deformation, and releasing a new expressive monocular dataset, Dy...

  5. Self-Learning Expression Deformations for Data-Efficient Gaussian Avatars

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    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.

  6. GRMM: Real-Time High-Fidelity Gaussian Morphable Head Model with Learned Residuals

    cs.GR 2025-09 conditional novelty 6.0 of 10

    GRMM combines a classic 3D face template with learned fine detail residuals to render controllable full-head avatars in real time.

  7. HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars

    cs.CV 2025-07 conditional novelty 6.0 of 10

    HairCUP trains a universal 3D avatar prior with separately modeled face and hair, using synthetic bald images, enabling hairstyle swapping and few-shot personalization.

  8. S-Avatar: Diffusion-Guided Gaussian Head Avatars from a Single Image

    cs.CV 2026-07 conditional novelty 5.0 of 10

    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.

  9. VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis

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    VisualSpeaker supervises photorealistic 3D Gaussian Splatting renders with a pretrained lip-reading model, reducing lip vertex error by 56.1% on MEAD versus an ablated baseline and improving perceived lip clarity in a...

  10. GGTalker: Talking Head Systhesis with Generalizable Gaussian Priors and Identity-Specific Adaptation

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    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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