URHead: A Unified UV-Space Representation for Joint Mesh-3DGS Optimization in Head Avatars
- URL: http://arxiv.org/abs/2607.22673v1
- Date: Wed, 08 Jul 2026 04:39:05 GMT
- Title: URHead: A Unified UV-Space Representation for Joint Mesh-3DGS Optimization in Head Avatars
- Abstract summary: We present URHead, a unified representation for high-fidelity and animatable head avatars.<n>Our key contribution is a UV-space unification where both representations share a common UV parameterization.<n> URHead maintains full controllability while preserving subject-specific details, and outperforms existing state-of-the-art methods in parametric reconstruction quality and animation consistency.
- Score: 26.674926584560094
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present URHead, a unified representation for high-fidelity and animatable head avatars that fundamentally redefines mesh-Gaussian integration. While mesh-based methods offer precise geometric control but lack photorealistic detail, and Gaussian-based approaches achieve photorealism but suffer from poor structural consistency, existing hybrid solutions fail to fully leverage their complementary strengths. Our key contribution is a UV-space unification where both representations share a common UV parameterization. Through joint optimization with adaptive gaussian sampling, our method automatically learns to disentangle and allocate appropriate roles to each component. URHead maintains full parametric controllability while preserving subject-specific details, and outperforms existing state-of-the-art methods in reconstruction quality and animation consistency.
Related papers
- SHARE: A Fully Unsupervised Framework for Single Hyperspectral Image Restoration [9.908527979711902]
Hyperspectral image (HSI) restoration is a fundamental challenge in computational imaging and computer vision.<n>This paper presents SHARE, a fully unsupervised framework that unifies geometric equivariance principles with low-rank spectral modelling.<n>Our method outperforms many state-of-the-art unsupervised approaches and achieves performance comparable to that of supervised methods.
arXiv Detail & Related papers (2026-01-20T14:01:13Z) - InpaintHuman: Reconstructing Occluded Humans with Multi-Scale UV Mapping and Identity-Preserving Diffusion Inpainting [64.42884719282323]
InpaintHuman is a novel method for generating high-fidelity, complete, and animatable avatars from occluded monocular videos.<n>Our approach employs direct pixel-level supervision to ensure identity fidelity.
arXiv Detail & Related papers (2026-01-05T13:26:02Z) - UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass [83.7071371474926]
UniSH is a unified, feed-forward framework for joint metric-scale 3D scene and human reconstruction.<n>Our framework bridges strong, disparate priors from scene reconstruction and HMR.<n>Our model achieves state-of-the-art performance on human-centric scene reconstruction.
arXiv Detail & Related papers (2026-01-03T16:06:27Z) - TexAvatars : Hybrid Texel-3D Representations for Stable Rigging of Photorealistic Gaussian Head Avatars [47.957612931386926]
TexAvatars is a hybrid representation that combines the explicit geometric grounding of analytic rigging with the spatial continuity of texel space.<n>Our approach predicts local geometric attributes in UV space via CNNs, but drives 3D deformation through mesh-aware Jacobians.<n>Our method achieves state-of-the-art performance under extreme pose and expression variations, demonstrating strong generalization in challenging head reenactment settings.
arXiv Detail & Related papers (2025-12-24T10:50:04Z) - STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstruction [18.777422532112105]
Existing methods based on 3D Gaussian Splatting typically bind Gaussians to mesh triangles and model deformations solely via Linear Blend Skinning.<n>We propose STAvatar, which consists of two key components: (1) a UV-Temporal Soft Binding framework that leverages both image-based and geometric priors to learn per-Gaussian feature offsets within the UV space.<n> STAvatar achieves state-of-the-art reconstruction performance, especially in capturing fine-grained details and reconstructing frequently occluded regions.
arXiv Detail & Related papers (2025-11-25T02:35:00Z) - VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment [48.147381011235446]
3D Gaussian Splatting has recently emerged as an efficient solution for real-time novel view synthesis.<n>We propose a novel method that enhances the geometric representation of 3D Gaussians through view alignment.<n>Our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.
arXiv Detail & Related papers (2025-10-13T14:44:50Z) - GDGS: 3D Gaussian Splatting Via Geometry-Guided Initialization And Dynamic Density Control [6.91367883100748]
Gaussian Splatting is an alternative for rendering realistic images while supporting real-time performance.<n>We propose a method to enhance 3D Gaussian Splatting (3DGS)citeKerbl2023, addressing challenges in initialization, optimization, and density control.<n>Our method demonstrates comparable or superior results to state-of-the-art methods, rendering high-fidelity images in real time.
arXiv Detail & Related papers (2025-07-01T01:29:31Z) - Hybrid Explicit Representation for Ultra-Realistic Head Avatars [55.829497543262214]
We introduce a novel approach to creating ultra-realistic head avatars and rendering them in real-time.<n> UV-mapped 3D mesh is utilized to capture sharp and rich textures on smooth surfaces, while 3D Gaussian Splatting is employed to represent complex geometric structures.<n>Experiments that our modeled results exceed those of state-of-the-art approaches.
arXiv Detail & Related papers (2024-03-18T04:01:26Z) - Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance.
We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring.
Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
arXiv Detail & Related papers (2023-05-22T12:18:20Z)
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.