Adds PolyGen to public Deepmind Research Github repository

PiperOrigin-RevId: 327802622

README.md

@@ -50,6 +50,8 @@ https://deepmind.com/research/publications/
 *   [Graph Matching Networks for Learning the Similarity of Graph Structured
     Objects](graph_matching_networks), ICML 2019
 *   [REGAL: Transfer Learning for Fast Optimization of Computation Graphs](regal)
+*   [PolyGen: PolyGen: An Autoregressive Generative Model of 3D Meshes](polygen), ICML 2020
+
 
 ## Disclaimer
 

polygen/README.md

@@ -0,0 +1,65 @@
+# PolyGen: An Autoregressive Generative Model of 3D Meshes
+
+![](media/example_samples.png)
+
+This package provides an implementation of PolyGen as described in:
+
+> **PolyGen: An Autoregressive Generative Model of 3D Meshes**, *Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia*, ICML, 2020. ([abs](https://arxiv.org/abs/2002.10880))
+
+PolyGen is a generative model of 3D meshes that sequentially outputs mesh
+vertices and faces. PolyGen consists of two parts: A vertex model, that
+unconditionally models mesh vertices, and a face model, that models the mesh
+faces conditioned on input vertices. The vertex model uses a masked Transformer
+decoder to express a distribution over the vertex sequences. For the face model
+we combine Transformers with [pointer networks](https://arxiv.org/abs/1506.03134)
+to express a distribution over variable length vertex sequences.
+
+In this repository we provide model code in `modules.py`, as well as data
+processing utilities in `data_utils.py`. We also provide Colabs that demo
+training PolyGen from scratch on a toy dataset, as well as sampling from a
+pre-trained model.
+
+There are some minor differences between this implementation and the paper:
+* We add global information (e.g. class label embeddings) as an additional input in the first sequence position rather than project it at each layer. This reduces parameters, but does not significantly impact performance.
+* We use [ReZero](https://arxiv.org/abs/2003.04887) which improves training speed.
+* We train with only shifting augmentations, which we find to be as effective as the combination of augmentations described in the paper. This helps to simplify the data pre-processing pipeline.
+
+## Training Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/polygen/training.ipynb)
+
+To train a PolyGen model from scratch on a collection of simple meshes use this
+colab. This demonstrates the data pre-processing required to create inputs for
+the vertex and face models.
+
+## Sampling pre-trained model Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepmind/deepmind-research/blob/master/polygen/sample-pretrained.ipynb)
+
+To sample a model pre-trained on [ShapeNet](https://www.shapenet.org/)
+use this colab. The model is class-conditional, and is trained on longer
+sequence lengths than those described in the paper. This colab uses the
+following checkpoints: ([Google Cloud Storage
+bucket](https://console.cloud.google.com/storage/browser/deepmind-research-polygen)).
+
+## Installation
+
+To install the package locally run:
+```bash
+git clone https://github.com/deepmind/deepmind-research.git .
+cd deepmind-research/polygen
+pip install -e .
+```
+
+## Giving Credit
+
+If you use this code in your work, we ask you to cite this paper:
+
+```
+@article{nash2020polygen,
+  author={Charlie Nash and Yaroslav Ganin and S. M. Ali Eslami and Peter W. Battaglia},
+  title={PolyGen: An Autoregressive Generative Model of 3D Meshes},
+  journal={ICML},
+  year={2020}
+}
+```
+
+## Disclaimer
+
+This is not an official Google product.