SEGA: Drivable 3D Gaussian Head Avatar from a Single Image

• URL: http://arxiv.org/abs/2504.14373v2
• Date: Wed, 23 Apr 2025 12:27:03 GMT
• Title: SEGA: Drivable 3D Gaussian Head Avatar from a Single Image
• Authors: Chen Guo, Zhuo Su, Jian Wang, Shuang Li, Xu Chang, Zhaohu Li, Yang Zhao, Guidong Wang, Ruqi Huang,
• Abstract summary: We propose SEGA, a novel approach for 3D drivable Gaussian head Avatar creation.<n>SEGA seamlessly combines priors derived from large-scale 2D datasets with 3D priors learned from multi-view, multi-expression, and multi-ID data.<n>Experiments show our method outperforms state-of-the-art approaches in generalization ability, identity preservation, and expression realism.
• Score: 15.117619290414064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creating photorealistic 3D head avatars from limited input has become increasingly important for applications in virtual reality, telepresence, and digital entertainment. While recent advances like neural rendering and 3D Gaussian splatting have enabled high-quality digital human avatar creation and animation, most methods rely on multiple images or multi-view inputs, limiting their practicality for real-world use. In this paper, we propose SEGA, a novel approach for Single-imagE-based 3D drivable Gaussian head Avatar creation that combines generalized prior models with a new hierarchical UV-space Gaussian Splatting framework. SEGA seamlessly combines priors derived from large-scale 2D datasets with 3D priors learned from multi-view, multi-expression, and multi-ID data, achieving robust generalization to unseen identities while ensuring 3D consistency across novel viewpoints and expressions. We further present a hierarchical UV-space Gaussian Splatting framework that leverages FLAME-based structural priors and employs a dual-branch architecture to disentangle dynamic and static facial components effectively. The dynamic branch encodes expression-driven fine details, while the static branch focuses on expression-invariant regions, enabling efficient parameter inference and precomputation. This design maximizes the utility of limited 3D data and achieves real-time performance for animation and rendering. Additionally, SEGA performs person-specific fine-tuning to further enhance the fidelity and realism of the generated avatars. Experiments show our method outperforms state-of-the-art approaches in generalization ability, identity preservation, and expression realism, advancing one-shot avatar creation for practical applications.

Related papers

• Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance [69.9745497000557]
  We introduce Arc2Avatar, the first SDS-based method utilizing a human face foundation model as guidance with just a single image as input.<n>Our avatars maintain a dense correspondence with a human face mesh template, allowing blendshape-based expression generation.
  arXiv  Detail & Related papers  (2025-01-09T17:04:33Z)
• Generalizable and Animatable Gaussian Head Avatar [50.34788590904843]
  We propose Generalizable and Animatable Gaussian head Avatar (GAGAvatar) for one-shot animatable head avatar reconstruction. We generate the parameters of 3D Gaussians from a single image in a single forward pass. Our method exhibits superior performance compared to previous methods in terms of reconstruction quality and expression accuracy.
  arXiv  Detail & Related papers  (2024-10-10T14:29:00Z)
• HeadGAP: Few-Shot 3D Head Avatar via Generalizable Gaussian Priors [24.245586597913082]
  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.<n>We propose a framework comprising prior learning and avatar creation phases.<n>Our model effectively exploits head priors and successfully generalizes them to few-shot personalization.
  arXiv  Detail & Related papers  (2024-08-12T09:19:38Z)
• NPGA: Neural Parametric Gaussian Avatars [46.52887358194364]
  We propose a data-driven approach to create high-fidelity controllable avatars from multi-view video recordings. We build our method around 3D Gaussian splatting for its highly efficient rendering and to inherit the topological flexibility of point clouds. We evaluate our method on the public NeRSemble dataset, demonstrating that NPGA significantly outperforms the previous state-of-the-art avatars on the self-reenactment task by 2.6 PSNR.
  arXiv  Detail & Related papers  (2024-05-29T17:58:09Z)
• GPAvatar: Generalizable and Precise Head Avatar from Image(s) [71.555405205039]
  GPAvatar is a framework that reconstructs 3D head avatars from one or several images in a single forward pass. The proposed method achieves faithful identity reconstruction, precise expression control, and multi-view consistency.
  arXiv  Detail & Related papers  (2024-01-18T18:56:34Z)
• GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians [51.46168990249278]
  We present an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video. GustafAvatar is validated on both the public dataset and our collected dataset.
  arXiv  Detail & Related papers  (2023-12-04T18:55:45Z)
• InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars [40.10906393484584]
  We propose a novel framework that enhances avatar reconstruction performance using an algorithm designed to increase the fidelity from multiple frames. Our architecture emphasizes pixel-aligned image-to-image translation, mitigating the need to learn correspondences between observation and canonical spaces. The proposed paradigm demonstrates state-of-the-art performance on one-shot and few-shot avatar animation tasks.
  arXiv  Detail & Related papers  (2023-12-03T18:59:15Z)
• Generalizable One-shot Neural Head Avatar [90.50492165284724]
  We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image. We propose a framework that not only generalizes to unseen identities based on a single-view image, but also captures characteristic details within and beyond the face area.
  arXiv  Detail & Related papers  (2023-06-14T22:33:09Z)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.