DeepMesh

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DeepMesh is an open-source research tool that enables auto-regressive artist-mesh creation using reinforcement learning. It generates high-quality 3D meshes conditioned on point cloud data.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is DeepMesh?

DeepMesh is an open-source project providing the official code for an auto-regressive artist-mesh creation system powered by reinforcement learning. Developed by researchers from Tsinghua University, ShengShu, and S-Lab at Nanyang Technological University, DeepMesh can generate high-quality 3D meshes based on given point cloud data using an auto-regressive transformer. The project has released a pretrained weight of DeepMesh (0.5 B) and optimized its inference code for a 50% reduction in generation time. It is designed for researchers and developers in the field of generative AI and 3D modeling, offering a framework for advanced mesh generation.

Best used for

Ideal for professors who need to conduct research in 3D generative AI, create artist-meshes from point cloud data, and implement reinforcement learning for mesh creation. Especially valuable for academic exploration and development of advanced 3D modeling techniques.

Common actions

generate 3D meshes

create artist meshes

research generative AI

implement reinforcement learning

automated workflowcollaborationlow-code/no-codeopen-sourcedeepfakeworkflowsface swapping"AI Agents"github copilot

Capabilities

Key features

Auto-regressive mesh creation
Reinforcement learning
Point cloud conditioning
Pretrained model weights
Optimized inference code

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of input does DeepMesh require for mesh generation?

DeepMesh requires point cloud data as input for generating meshes. Users should ensure that the normal information is included with their point cloud files to achieve optimal results. The tool can process various common 3D file formats like OBJ and PLY.

What are the system requirements for running DeepMesh?

DeepMesh has been tested on Ubuntu 22 with CUDA 11.8, using NVIDIA A100, A800, and A6000 GPUs. It also supports CUDA 12.1 with specific PyTorch and xformers versions. A Python 3.12 environment is recommended for installation.

Are there plans for future releases or features for DeepMesh?

Yes, the developers plan to release the pre-training code (truncated sliding training) and post-training code (DPO). They also intend to release a larger model, specifically a 1 billion parameter version, to enhance the tool's capabilities.

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