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lucidrains / meshgpt-pytorch

Implementation of MeshGPT, SOTA Mesh generation using Attention, in Pytorch
MIT License
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artificial-intelligence attention-mechanisms deep-learning mesh-generation transformers

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MeshGPT - Pytorch

Implementation of MeshGPT, SOTA Mesh generation using Attention, in Pytorch

Will also add text conditioning, for eventual text-to-3d asset

Please join Join us on Discord if you are interested in collaborating with others to replicate this work

Update: Marcus has trained and uploaded a working model to 🤗 Huggingface!

Appreciation

Install

$ pip install meshgpt-pytorch

Usage

import torch

from meshgpt_pytorch import (
    MeshAutoencoder,
    MeshTransformer
)

# autoencoder

autoencoder = MeshAutoencoder(
    num_discrete_coors = 128
)

# mock inputs

vertices = torch.randn((2, 121, 3))            # (batch, num vertices, coor (3))
faces = torch.randint(0, 121, (2, 64, 3))      # (batch, num faces, vertices (3))

# make sure faces are padded with `-1` for variable lengthed meshes

# forward in the faces

loss = autoencoder(
    vertices = vertices,
    faces = faces
)

loss.backward()

# after much training...
# you can pass in the raw face data above to train a transformer to model this sequence of face vertices

transformer = MeshTransformer(
    autoencoder,
    dim = 512,
    max_seq_len = 768
)

loss = transformer(
    vertices = vertices,
    faces = faces
)

loss.backward()

# after much training of transformer, you can now sample novel 3d assets

faces_coordinates, face_mask = transformer.generate()

# (batch, num faces, vertices (3), coordinates (3)), (batch, num faces)
# now post process for the generated 3d asset

For text-conditioned 3d shape synthesis, simply set condition_on_text = True on your MeshTransformer, and then pass in your list of descriptions as the texts keyword argument

ex.

transformer = MeshTransformer(
    autoencoder,
    dim = 512,
    max_seq_len = 768,
    condition_on_text = True
)

loss = transformer(
    vertices = vertices,
    faces = faces,
    texts = ['a high chair', 'a small teapot'],
)

loss.backward()

# after much training of transformer, you can now sample novel 3d assets conditioned on text

faces_coordinates, face_mask = transformer.generate(
    texts = ['a long table'],
    cond_scale = 8.,  # a cond_scale > 1. will enable classifier free guidance - can be placed anywhere from 3. - 10.
    remove_parallel_component = True # from https://arxiv.org/abs/2410.02416
)

If you want to tokenize meshes, for use in your multimodal transformer, simply invoke .tokenize on your autoencoder (or same method on autoencoder trainer instance for the exponentially smoothed model)


mesh_token_ids = autoencoder.tokenize(
    vertices = vertices,
    faces = faces
)

# (batch, num face vertices, residual quantized layer)

Typecheck

At the project root, run

$ cp .env.sample .env

Todo

Citations

@inproceedings{Siddiqui2023MeshGPTGT,
    title   = {MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers},
    author  = {Yawar Siddiqui and Antonio Alliegro and Alexey Artemov and Tatiana Tommasi and Daniele Sirigatti and Vladislav Rosov and Angela Dai and Matthias Nie{\ss}ner},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:265457242}
}
@inproceedings{dao2022flashattention,
    title   = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
    author  = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
    booktitle = {Advances in Neural Information Processing Systems},
    year    = {2022}
}
@inproceedings{Leviathan2022FastIF,
    title   = {Fast Inference from Transformers via Speculative Decoding},
    author  = {Yaniv Leviathan and Matan Kalman and Y. Matias},
    booktitle = {International Conference on Machine Learning},
    year    = {2022},
    url     = {https://api.semanticscholar.org/CorpusID:254096365}
}
@misc{yu2023language,
    title   = {Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation}, 
    author  = {Lijun Yu and José Lezama and Nitesh B. Gundavarapu and Luca Versari and Kihyuk Sohn and David Minnen and Yong Cheng and Agrim Gupta and Xiuye Gu and Alexander G. Hauptmann and Boqing Gong and Ming-Hsuan Yang and Irfan Essa and David A. Ross and Lu Jiang},
    year    = {2023},
    eprint  = {2310.05737},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
@article{Lee2022AutoregressiveIG,
    title   = {Autoregressive Image Generation using Residual Quantization},
    author  = {Doyup Lee and Chiheon Kim and Saehoon Kim and Minsu Cho and Wook-Shin Han},
    journal = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year    = {2022},
    pages   = {11513-11522},
    url     = {https://api.semanticscholar.org/CorpusID:247244535}
}
@inproceedings{Katsch2023GateLoopFD,
    title   = {GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling},
    author  = {Tobias Katsch},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:265018962}
}