TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2411.19654v1
- Date: Fri, 29 Nov 2024 12:19:39 GMT
- Title: TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting
- Authors: Bojun Xiong, Jialun Liu, Jiakui Hu, Chenming Wu, Jinbo Wu, Xing Liu, Chen Zhao, Errui Ding, Zhouhui Lian,
- Abstract summary: This paper presents TexGaussian, a novel method that uses octant-aligned 3D Gaussian Splatting for rapid PBR material generation.
Our method synthesizes more visually pleasing PBR materials and runs faster than previous methods in both unconditional and text-conditional scenarios.
- Score: 48.97819552366636
- License:
- Abstract: Physically Based Rendering (PBR) materials play a crucial role in modern graphics, enabling photorealistic rendering across diverse environment maps. Developing an effective and efficient algorithm that is capable of automatically generating high-quality PBR materials rather than RGB texture for 3D meshes can significantly streamline the 3D content creation. Most existing methods leverage pre-trained 2D diffusion models for multi-view image synthesis, which often leads to severe inconsistency between the generated textures and input 3D meshes. This paper presents TexGaussian, a novel method that uses octant-aligned 3D Gaussian Splatting for rapid PBR material generation. Specifically, we place each 3D Gaussian on the finest leaf node of the octree built from the input 3D mesh to render the multiview images not only for the albedo map but also for roughness and metallic. Moreover, our model is trained in a regression manner instead of diffusion denoising, capable of generating the PBR material for a 3D mesh in a single feed-forward process. Extensive experiments on publicly available benchmarks demonstrate that our method synthesizes more visually pleasing PBR materials and runs faster than previous methods in both unconditional and text-conditional scenarios, which exhibit better consistency with the given geometry. Our code and trained models are available at https://3d-aigc.github.io/TexGaussian.
Related papers
- Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation [58.77520205498394]
This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts.
The framework consists of 3D shape generation and texture generation.
This report details the system architecture, experimental results, and potential future directions to improve and expand the framework.
arXiv Detail & Related papers (2025-02-20T04:22:30Z) - F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Consistent Gaussian Splatting [35.625593119642424]
This paper tackles the problem of generalizable 3D-aware generation from monocular datasets.
We propose a novel feed-forward pipeline based on pixel-aligned Gaussian Splatting.
We also introduce a self-supervised cycle-consistent constraint to enforce cross-view consistency in the learned 3D representation.
arXiv Detail & Related papers (2025-01-12T04:44:44Z) - GraphicsDreamer: Image to 3D Generation with Physical Consistency [32.26851174969898]
We introduce GraphicsDreamer, a method for creating highly usable 3D meshes from single images.
In the geometry fusion stage, we continue to enforce the PBR constraints, ensuring that the generated 3D objects possess reliable texture details.
Our method incorporates topology optimization and fast UV unwrapping capabilities, allowing the 3D products to be seamlessly imported into graphics engines.
arXiv Detail & Related papers (2024-12-18T10:01:27Z) - AGG: Amortized Generative 3D Gaussians for Single Image to 3D [108.38567665695027]
We introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image.
AGG decomposes the generation of 3D Gaussian locations and other appearance attributes for joint optimization.
We propose a cascaded pipeline that first generates a coarse representation of the 3D data and later upsamples it with a 3D Gaussian super-resolution module.
arXiv Detail & Related papers (2024-01-08T18:56:33Z) - Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D
Reconstruction with Transformers [37.14235383028582]
We introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference.
Our method utilizes two transformer-based networks, namely a point decoder and a triplane decoder, to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation.
arXiv Detail & Related papers (2023-12-14T17:18:34Z) - TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion
Models [77.85129451435704]
We present a new method to synthesize textures for 3D, using large-scale-guided image diffusion models.
Specifically, we leverage latent diffusion models, apply the set denoising model and aggregate denoising text map.
arXiv Detail & Related papers (2023-10-20T19:15:29Z) - 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) - DreamFusion: Text-to-3D using 2D Diffusion [52.52529213936283]
Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs.
In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis.
Our approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors.
arXiv Detail & Related papers (2022-09-29T17:50:40Z) - On Demand Solid Texture Synthesis Using Deep 3D Networks [3.1542695050861544]
This paper describes a novel approach for on demand texture synthesis based on a deep learning framework.
A generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes.
The synthesized volumes have good visual results that are at least equivalent to the state-of-the-art patch based approaches.
arXiv Detail & Related papers (2020-01-13T20:59:14Z)
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.