DoubleDiffusion: Combining Heat Diffusion with Denoising Diffusion for Texture Generation on 3D Meshes
The Australian National University（澳大利亚国立大学）；Tencent XR Lab（腾讯XR实验室）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Monash University（莫纳什大学）
Xuyang Wang, Ziang Cheng, Zhenyu Li, Jiayu Yang, Haorui Ji, Pan Ji, Mehrtash Harandi, Richard Hartley, Hongdong Li
This paper addresses the problem of generating textures for 3D mesh assets. Existing approaches often rely on image diffusion models to generate multi-view image observations, which are then transformed onto the mesh surface to produce a single texture. However, due to the gap between multi-view images and 3D space, such process is susceptible to arange of issues such as geometric inconsistencies, visibility occlusion, and baking artifacts. To overcome this problem, we propose a novel approach that directly generates texture on 3D meshes. Our approach leverages heat dissipation diffusion, which serves as an efficient operator that propagates features on the geometric surface of a mesh, while remaining insensitive to the specific layout of the wireframe. By integrating this technique into a generative diffusion pipeline, we significantly improve the efficiency of texture generation compared to existing texture generation methods. We term our approach DoubleDiffusion, as it combines heat dissipation diffusion with denoising diffusion to enable native generative learning on 3D mesh surfaces.
In this paper, we present a face fairness framework for 3D meshes that preserves the regular shape of faces and is applicable to a variety of 3D mesh restoration tasks. Specifically, we present a number of desirable properties for any mesh restoration method and show that our framework satisfies them. We then apply our framework to two different tasks --- mesh-denoising and mesh-refinement, and present comparative results for these two tasks showing improvement over other relevant methods in the literature.
NVIDIA（英伟达）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）
Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes. To this end, we introduce the hierarchical Latent Point Diffusion Model (LION) for 3D shape generation. LION is set up as a variational autoencoder (VAE) with a hierarchical latent space that combines a global shape latent representation with…
Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D scenes from multi-view images. Most methods rely on simple rendering algorithms: pre-filtered direct lighting or learned representations of irradiance. We show that a more realistic shading model, incorporating ray tracing and Monte Carlo integration, substantially improves decomposition into shape, materials & lighting. Unfortunately, Monte Carlo integration provides estimates with significant noise, even at large sample counts, which makes gradient-based inverse rendering very challenging. To address t…
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation
Center for Virtual Imaging Trials, Department of Radiology, Duke University School of Medicine, Durham, NC, USA Biomedical Imaging Group Rotterdam, Department of Radiology \& Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands Electrical；Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC, USA
TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Baidu VIS（百度VIS）；Institute of Medical Technology, Peking University（北京大学医学技术研究所）
MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving
MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院教育部人工智能重点实验室）；Academy of Military Science（军事科学院）；Bosch innovation software development (Wuxi) Co., Ltd.（博世创新软件开发（无锡）有限公司）；College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；Shopee Pte. Ltd.（Shopee私人有限公司）
