MeshDiffusion: Score-based Generative 3D Mesh Modeling

1Mila, Université de Montréal, 2Max Planck Institute for Intelligent Systems,
3ETH Zürich, 4McGill University, 5University of Cambridge

We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overly-smooth or noisy surfaces without fine-grained geometric details. To overcome these shortcomings, we take advantage of the graph structure of meshes and use a simple yet very effective generative modeling method to generate 3D meshes. Specifically, we represent meshes with deformable tetrahedral grids, and then train a diffusion model on this direct parameterization. We demonstrate the effectiveness of our model on multiple generative tasks.

Method Overview

MeshDiffusion uses a 3D diffusion model to generate 3D meshes parametrized by deformable marching tetrahedra (DMTets).


Unconditional Generation

With standard DDPM training and sampling, MeshDiffusion can generate realistic and diverse sets of 3D meshes, many of which are novel shapes not in the training set.

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Single-view Conditional Generation

Provided with an incomplete DMTet fitted on a single 2.5D view, MeshDiffusion can generate the full 3D mesh by completing the occluded regions.


By using DDIM as the diffusion model sampler, the initial noise can be interpolated to create interpolated shapes.


The generated meshes by MeshDiffusion can be textured by using state-of-the-arts methods like TEXTure.


We thank Yuliang Xiu, Jinlong Yang, Tim Xiao, Haiwen Feng, Yandong Wen for constructive suggestions. We would like to thank Samsung Electronics Co., Ldt. for funding this research.
Disclosure. MJB has received research gift funds from Adobe, Intel, Nvidia, Meta/Facebook, and Amazon. MJB has financial interests in Amazon, Datagen Technologies, and Meshcapade GmbH. While MJB is a part-time employee of Meshcapade, his research was performed solely at, and funded solely by, the Max Planck Society. DN is supported by NSERC Discovery Grant (RGPIN-5011360) and LP is supported by NSERC Discovery Grant (RGPIN-04653).


    title={MeshDiffusion: Score-based Generative 3D Mesh Modeling},
    author={Zhen Liu and Yao Feng and Michael J. Black and Derek Nowrouzezahrai and Liam Paull and Weiyang Liu},
    booktitle={International Conference on Learning Representations},