kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
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Autor | SHA1 | Data |
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Zhen Liu | 296632d76c | |
Zhen Liu | 6eae91c818 | |
Zhen Liu | 8448ae5e31 | |
Zhen Liu | 4ed7bde39d | |
Zhen Liu | 8351e74575 | |
Zhen Liu | 219c491f9e | |
Zhen Liu | 8cfa2dece7 | |
Zhen Liu | 7c68b0027f |
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@ -31,9 +31,13 @@ Follow the instructions to install requirements for [nvdiffrec](https://github.c
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### Pretrained Models
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Download the model checkpoints from [Google Drive](https://drive.google.com/drive/folders/15IjbUM1tQf8gS0YsRqY5ZbMs-leJgoJ0?usp=sharing).
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Download our pretrained MeshDiffusion models (resolution 64) for [chair](https://keeper.mpdl.mpg.de/f/95640f5bd3764a44b907/?dl=1), [car](https://keeper.mpdl.mpg.de/f/061265ef78df494baaf5/?dl=1) and [airplane](https://keeper.mpdl.mpg.de/f/f5074d6b0cb24445a80d/?dl=1). As a backup option, you can also download the models for car and chair from [Google Drive](https://drive.google.com/drive/folders/15IjbUM1tQf8gS0YsRqY5ZbMs-leJgoJ0?usp=sharing).
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Download our pretrained MeshDiffusion model for [chair](https://keeper.mpdl.mpg.de/f/95640f5bd3764a44b907/?dl=1), [car](https://keeper.mpdl.mpg.de/f/061265ef78df494baaf5/?dl=1) and [airplane](https://keeper.mpdl.mpg.de/f/f5074d6b0cb24445a80d/?dl=1).
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Download the res-128 models here: [car](https://huggingface.co/lzzcd001/MeshDiffusion_models/blob/main/car_res128.pt) and [chair](https://huggingface.co/lzzcd001/MeshDiffusion_models/blob/main/chair_res128.pt).
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### Datasets
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We provide processed datasets (in the form of cubic grids) of resolution 64 in this [link](https://huggingface.co/datasets/lzzcd001/MeshDiffusion_DMTet_Dataset). The deformation scale for the datasets is set to 3.0, and the SDF values of all non-mesh-generating tetrahedral vertices are set to either 1 or -1 (depending on their signs), as described in the paper. The cubic grids with the boundary removed are of size 63x63x63 and padded on the right to 64x64x64 for convenience. Please check `eval.py` to see how to extract DMTet representations from the 3D cubic grids.
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## Inference
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