Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting

• URL: http://arxiv.org/abs/2504.20403v1
• Date: Tue, 29 Apr 2025 03:56:36 GMT
• Title: Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting
• Authors: Hanxi Liu, Yifang Men, Zhouhui Lian,
• Abstract summary: We introduce a framework that decouples the editing process into local spatial adaptation and realistic appearance learning.<n>The framework combines the controllable explicit structure of tetrahedral grids with the high-precision rendering capabilities of 3D Gaussian Splatting.<n>Both qualitative and quantitative experiments demonstrate the effectiveness and superiority of our approach in generating photorealistic 3D editable avatars.
• Score: 17.908135908777325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized 3D avatar editing holds significant promise due to its user-friendliness and availability to applications such as AR/VR and virtual try-ons. Previous studies have explored the feasibility of 3D editing, but often struggle to generate visually pleasing results, possibly due to the unstable representation learning under mixed optimization of geometry and texture in complicated reconstructed scenarios. In this paper, we aim to provide an accessible solution for ordinary users to create their editable 3D avatars with precise region localization, geometric adaptability, and photorealistic renderings. To tackle this challenge, we introduce a meticulously designed framework that decouples the editing process into local spatial adaptation and realistic appearance learning, utilizing a hybrid Tetrahedron-constrained Gaussian Splatting (TetGS) as the underlying representation. TetGS combines the controllable explicit structure of tetrahedral grids with the high-precision rendering capabilities of 3D Gaussian Splatting and is optimized in a progressive manner comprising three stages: 3D avatar instantiation from real-world monocular videos to provide accurate priors for TetGS initialization; localized spatial adaptation with explicitly partitioned tetrahedrons to guide the redistribution of Gaussian kernels; and geometry-based appearance generation with a coarse-to-fine activation strategy. Both qualitative and quantitative experiments demonstrate the effectiveness and superiority of our approach in generating photorealistic 3D editable avatars.

Related papers

• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• DirectTriGS: Triplane-based Gaussian Splatting Field Representation for 3D Generation [37.09199962653554]
  We present DirectTriGS, a novel framework designed for 3D object generation with Gaussian Splatting (GS)<n>The proposed generation framework can produce high-quality 3D object geometry and rendering results in the text-to-3D task.
  arXiv  Detail & Related papers  (2025-03-10T04:05:38Z)
• Generating Editable Head Avatars with 3D Gaussian GANs [57.51487984425395]
  Traditional 3D-aware generative adversarial networks (GANs) achieve photorealistic and view-consistent 3D head synthesis.<n>We propose a novel approach that enhances the editability and animation control of 3D head avatars by incorporating 3D Gaussian Splatting (3DGS) as an explicit 3D representation.<n>Our approach delivers high-quality 3D-aware synthesis with state-of-the-art controllability.
  arXiv  Detail & Related papers  (2024-12-26T10:10:03Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• 3D Gaussian Editing with A Single Image [19.662680524312027]
  We introduce a novel single-image-driven 3D scene editing approach based on 3D Gaussian Splatting. Our method learns to optimize the 3D Gaussians to align with an edited version of the image rendered from a user-specified viewpoint. Experiments show the effectiveness of our method in handling geometric details, long-range, and non-rigid deformation.
  arXiv  Detail & Related papers  (2024-08-14T13:17:42Z)
• WE-GS: An In-the-wild Efficient 3D Gaussian Representation for Unconstrained Photo Collections [8.261637198675151]
  Novel View Synthesis (NVS) from unconstrained photo collections is challenging in computer graphics. We propose an efficient point-based differentiable rendering framework for scene reconstruction from photo collections. Our approach outperforms existing approaches on the rendering quality of novel view and appearance synthesis with high converge and rendering speed.
  arXiv  Detail & Related papers  (2024-06-04T15:17:37Z)
• 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)
• CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting [57.14748263512924]
  CG3D is a method for compositionally generating scalable 3D assets. Gamma radiance fields, parameterized to allow for compositions of objects, possess the capability to enable semantically and physically consistent scenes.
  arXiv  Detail & Related papers  (2023-11-29T18:55:38Z)
• High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
  We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
  arXiv  Detail & Related papers  (2022-11-28T18:59:52Z)

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.