OMEGA-Avatar: One-shot Modeling of 360° Gaussian Avatars

Zehao Xia; Yiqun Wang; Zhengda Lu; Kai Liu; Jun Xiao; Peter Wonka

cs.GRFeb 12, 2026

OMEGA-Avatar: One-shot Modeling of 360° Gaussian Avatars

Authors: Zehao Xia, Yiqun Wang, Zhengda Lu, Kai Liu, Jun Xiao, Peter Wonka

Organizations: Chongqing University, China · University of Chinese Academy of Sciences, China · KAUST, Saudi Arabia

Abstract

Creating high-fidelity, animatable 3D avatars from a single image remains a formidable challenge. We identified three desirable attributes of avatar generation: 1) the method should be feed-forward, 2) model a 360° full-head, and 3) should be animation-ready. However, current work addresses only two of the three points simultaneously. To address these limitations, we propose OMEGA-Avatar, the first feed-forward framework that simultaneously generates a generalizable, 360°-complete, and animatable 3D Gaussian head from a single image. Starting from a feed-forward and animatable framework, we address the 360° full-head avatar generation problem with two novel components. First, to overcome poor hair modeling in full-head avatar generation, we introduce a semantic-aware mesh deformation module that integrates multi-view normals to optimize a FLAME head with hair while preserving its topology structure. Second, to enable effective feed-forward decoding of full-head features, we propose a multi-view feature splatting module that constructs a shared canonical UV representation from features across multiple views through differentiable bilinear splatting, hierarchical UV mapping, and visibility-aware fusion. This approach preserves both global structural coherence and local high-frequency details across all viewpoints, ensuring 360° consistency without per-instance optimization. Extensive experiments demonstrate that OMEGA-Avatar achieves state-of-the-art performance, significantly outperforming existing baselines in 360° full-head completeness while robustly preserving identity across different viewpoints.

Figures & tables

Figure 1. Given a single portrait image across diverse styles (left), our method performs one-shot reconstruction of a high-fidelity full-head Gaussian avatar (middle). The reconstructed avatars include the back of the head and are capable of high-quality animations (right). teaser

Method	Representation	One-Shot	$360^{\circ}$ Full-Head	Animatable
Rodin ( Wang et al., 2023 )	Tri-plane	✓	✓	✗
PanoHead ( An et al., 2023 )	Tri-plane	✗ ∗	✓	✗
SphereHead ( Li et al., 2024 )	Tri-plane	✗ ∗	✓	✗
FaceLift ( Han et al., 2024 )	3DGS	✓	✓	✗
FATE ( Zhang et al., 2025 )	3DGS	✗	✓	✓
GAGAvatar ( Chu and Harada, 2024 )	3DGS	✓	✗	✓

Table 1. Comparison of properties with state-of-the-art methods. Our method is the first framework for one-shot reconstruction of animatable

360^{\circ}

full-head avatars with 3D Gaussian Splatting.

Figure 2. Pipeline Overview. Given the source and target images, we leverage diffusion models to synthesize multi-view RGB images and corresponding normal maps. These normal maps are used for semantic-aware mesh deformation, while pixel-wise features are extracted from multi-view RGB images. Multi-view features are subsequently aggregated into a canonical UV feature map through the multi-view feature splatting module. The UV features and vertex features extracted from the deformed mesh are decoded and anchored to the mesh via UV mapping. For animation, the expression and pose derived from the target image are injected into the deformed mesh. Finally, the rendered output is enhanced by a neural refiner to generate the final full-head avatar. A schematic pipeline diagram illustrating the full-head avatar generation process. The diagram shows source and target images as inputs, followed by diffusion-based synthesis of multi-view RGB images and normal maps. The normal maps guide mesh deformation, while features extracted from the multi-view images are aggregated into a canonical UV feature map through a feature splatting module. The UV features and mesh vertex features are decoded and mapped onto the deformed mesh. Expression and pose information from the target image are then applied to the mesh for animation, and the final rendered result is refined by a neural refinement module.

Figure 3. Semantic-aware Mesh Deformation. Direct optimization with normal guidance disrupts FLAME’s parametric structure, causing severe surface irregularities and topological artifacts (left). Note the cranial holes and degenerated facial features like eyes and ears (highlighted in red boxes). Our approach (right) mitigates this via semantic-aware topology preservation and a semantic-aware Laplacian. We preserve FLAME’s clean topology and ensure $360^{\circ}$ geometric consistency, enabling fine detail generation without compromising facial structural integrity.

Figure 4. Multi-view Feature Splatting. Taking a frontal view as an example, we first obtain per-pixel UV coordinates and normals via rasterization, mapping features to UV space using differentiable bilinear splatting. Our hierarchical UV mapping builds a multi-resolution pyramid to fill missing regions in a coarse-to-fine manner. Simultaneously, we calculate a fusion weight map by combining view-dependent confidence and UV sampling density. Finally, the visibility-aware fusion module aggregates weighted features across all views to generate the final UV feature map. Diagram illustrating the per-view feature processing pipeline. For each input view, rasterization yields per-pixel UV coordinates, followed by bilinear splatting of 2D features onto hierarchical UV maps. The resulting partial UV feature maps are merged via visibility-aware fusion to form a complete canonical UV feature map.

Figure 5. Novel view synthesis from single image on the Avatar-256 dataset. Compared to state-of-the-art methods, our approach produces high-quality rendering results, even under unseen and extreme side-view facial angles. Note that PanoHead and SphereHead require inputs aligned to the FFHQ canonical space, leading to apparent scale differences. We use the red boxes to highlight the visual artifacts. A qualitative comparison showing rendered head avatars from multiple methods under extreme side-view facial angles. The visual results illustrate differences in identity consistency and rendering quality across methods, with the proposed approach producing more stable facial structure and fewer visual artifacts in challenging viewpoints.

	Avatar-256 Novel Views					NeRSemble Novel Views
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	CSIM $\uparrow$	DS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	CSIM $\uparrow$	DS $\downarrow$
PanoHead	17.995	0.5932	0.2592	0.4556	0.1663	17.151	0.6098	0.2706	0.5742	0.1082
SphereHead	18.932	0.6238	0.2436	0.4714	0.1509	17.446	0.6183	0.2628	0.5781	0.1053
GAGAvatar	22.802	0.7714	0.1719	0.4682	0.1762	22.556	0.8027	0.1505	0.6867	0.1033
SOAP	21.335	0.7287	0.1948	0.5783	0.1890	19.539	0.7471	0.1975	0.6855	0.1128
LAM	21.802	0.7314	0.1997	0.4842	0.1818	20.263	0.7465	0.1853	0.6321	0.1118

Table 2. Quantitative Comparison of Novel View Synthesis on the Avatar-256 and NeRSemble Datasets. Colors denote the best and second-best

Figure 6. Qualitative Ablation on the NeRSemble dataset. We compare results for models trained: (1) without multi-view diffusion, (2) without using the semantic-aware Laplacian term, (3) without differentiable bilinear splatting, (4) without hierarchical UV mapping, (5) without visibility-aware fusion. Ablation

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	CSIM $\uparrow$	AED $\downarrow$	APD $\downarrow$	AKD $\downarrow$
SOAP	14.557	0.599	0.305	0.662	0.175	0.0562	4.997
Ours (w/o refiner)	18.062	0.714	0.188	0.664	0.116	0.0207	2.196
Ours (full)	20.739	0.744	0.169	0.660	0.109	0.0188	2.023

Table 3. Reenactment evaluation on the VFHQ dataset.

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	CSIM $\uparrow$	AED $\downarrow$	APD $\downarrow$	AKD $\downarrow$
SOAP	14.607	0.711	0.283	0.735	0.166	0.0351	3.579
Ours (w/o refiner)	19.854	0.765	0.166	0.757	0.107	0.0090	1.551
Ours (full)	22.594	0.820	0.123	0.752	0.097	0.0091	1.515

Table 4. Reenactment evaluation on the HDTF dataset.

	Ablation Novel Views
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	CSIM $\uparrow$	DS $\downarrow$
w/o MV Diffusion	22.687	0.774	0.151	0.655	0.124
w/o S.a Laplacian	22.988	0.782	0.150	0.667	0.105
w/o D.B Splatting	23.187	0.789	0.141	0.643	0.098
w/o Hierarchical UV	22.997	0.785	0.149	0.655	0.103
w/o V.a fusion	23.121	0.793	0.151	0.597	0.110

Table 5. Ablation of different components on the NeRSemble dataset.

Figure 7. Additional Results on In-the-wild Images. Our method demonstrates great generalization ability and robustness towards in-the-wild images, and provides realistic unseen view rendering results. Red boxes highlight visual artifacts.

Figure 8. More Visual results on the NeRSemble dataset. Taking a single view as input, we perform $360^{\circ}$ novel view synthesis to compare our method with state-of-the-art approaches. The results show that our method achieves superior multi-view consistency and accurately reconstructs unseen regions. We use the red boxes to highlight the visual artifacts.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	CSIM $\uparrow$	AED $\downarrow$	APD $\downarrow$	AKD $\downarrow$
GAGAvatar ( Chu and Harada, 2024 )	21.83	0.818	0.122	0.816	0.111	0.135	3.349
LAM ( He et al., 2025 )	22.65	0.829	0.109	0.822	0.102	0.134	2.059
SOAP ( Liao et al., 2025 )	14.557	0.599	0.305	0.662	0.175	0.0562	4.997
Ours (w/o refiner)	18.062	0.714	0.188	0.664	0.116	0.0207	2.196
Ours (full)	20.739	0.744	0.169	0.660	0.109	0.0188	2.023

Appendix

Table 6. Self-reenactment comparison on the VFHQ dataset. Colors denote the best and second-best results.

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	CSIM $\uparrow$	AED $\downarrow$	APD $\downarrow$	AKD $\downarrow$
GAGAvatar ( Chu and Harada, 2024 )	23.13	0.863	0.103	0.862	0.110	0.111	2.985
LAM ( He et al., 2025 )	23.43	0.873	0.097	0.865	0.101	0.093	1.965
SOAP ( Liao et al., 2025 )	14.607	0.711	0.283	0.735	0.166	0.0351	3.579
Ours (w/o refiner)	19.854	0.765	0.166	0.757	0.107	0.0090	1.551
Ours (full)	22.594	0.820	0.123	0.752	0.097	0.0091	1.515

Appendix

Table 7. Self-reenactment comparison on the HDTF dataset. Colors denote the best and second-best results.

Index	NeRSemble ID	Avatar-256 ID
1	017	20220614--1135--DNM410
2	070	20220808--0809--DPE040
3	124	20230308--1352--BDF920
4	175	20230328--0800--BLY735
5	214	20230405--1635--AAN112
6	218	20230726--1657--AYE877

Appendix

Table 8. Identities used from the NeRSemble and Avatar-256 datasets.

Figure 9. Qualitative stress test for extreme-expression reenactment. The top row shows six driving images spanning joy, anger, rage, a manic grin, shock, and surprise, while the leftmost column shows the source portraits used to reconstruct the avatars. A grid with six driving expressions in the top row and ten source portraits in the left column. Each interior cell shows the corresponding source avatar reenacted with the expression of the driving image.

Figure 10. Additional qualitative results on the NeRSemble and Avatar-256 datasets. We use the red boxes to highlight the visual artifacts. Additional qualitative results on the NeRSemble and Avatar-256 datasets

Figure 11. Full-head reconstruction results on in-the-wild images Full-head reconstruction results on in-the-wild images

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Building one-shot 3D animatable head avatars is an important yet challenging problem. Existing methods generally collapse under large camera pose variations, compromising the realism of 3D avatars. In this work, we propose a new framework to tackle the novel setting of one-shot 3D full-head animatable avatar reconstruction in a single forward pass via inpainted UV-space Gaussian modeling, enabling 360

^\circ

rendering views and real-time animation. To facilitate efficient animation control, we model 3D head avatars with Gaussian primitives embedded on the surface of a parametric face model within the UV space, and project the input image features to the UV space, resulting in incomplete local UV feature maps. To inpaint the missing regions, we obtain knowledge of full-head geometry and textures from rich 3D full-head priors within a pretrained 3D generative adversarial network (GAN) for global full-head feature extraction and multi-view supervision. Specifically, to enhance the fidelity of 3D reconstruction during inpainting, we take advantage of the symmetric nature of the UV space and human faces to fuse incomplete yet detailed local UV feature maps with the extracted global full-head textures, resulting in inpainted UV Gaussian attribute maps for avatar modeling. Extensive experiments demonstrate that our method is the first to achieve high-quality 3D full-head animatable avatar modeling, significantly improving side and back views while outperforming state-of-the-art animation approaches, thereby improving the realism of 3D animatable avatars.

Abstract

3D Avatar Generation

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