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Oct 21, 2019
Oct 21, 2019
Oct 21, 2019
Oct 21, 2019

High resolution disentanglement datasets

This repository contains the Falcor3D and Isaac3D datasets, which present a state-of-the-art challenge for controllable generation in terms of image resolution, photorealism, and richness of style factors, as compared to existing disentanglement datasets.

Falor3D

The Falcor3D dataset consists of 233,280 images based on the 3D scene of a living room, where each image has a resolution of 1024x1024. The meta code corresponds to all possible combinations of 7 factors of variation:

  • lighting_intensity (5)
  • lighting_x-dir (6)
  • lighting_y-dir (6)
  • lighting_z-dir (6)
  • camera_x-pos (6)
  • camera_y-pos (6)
  • camera_z-pos (6)

Note that the number m behind each factor represents that the factor has m possible values, uniformly sampled in the normalized range of variations [0, 1].

Each image has as filename padded_index.png where

index = lighting_intensity * 46656 + lighting_x-dir * 7776 + lighting_y-dir * 1296 + 
lighting_z-dir * 216 + camera_x-pos * 36 + camera_y-pos * 6 + camera_z-pos

padded_index = index padded with zeros such that it has 6 digits.

To see the Falcor3D images by varying each factor of variation individually, you can run

python dataset_demo.py --dataset Falor3D

and the results are saved in the examples/falcor3d_samples folder.

You can also check out the Falcor3D images here: falcor3d_samples_demo, which includes all the ground-truth latent traversals.

Isaac3D

The Isaac3D dataset consists of 737,280 images, based on the 3D scene of a kitchen, where each image has a resolution of 512x512. The meta code corresponds to all possible combinations of 9 factors of variation:

  • object_shape (3)
  • object_scale (4)
  • camera_height (4)
  • robot_x-movement (8)
  • robot_y-movement (5)
  • lighting_intensity (4)
  • lighting_y-dir (6)
  • object_color (4)
  • wall_color (4)

Similarly, the number m behind each factor represents that the factor has m possible values, uniformly sampled in the normalized range of variations [0, 1].

Each image has as filename padded_index.png where

index = object_shape * 245760 + object_scale * 30720 + camera_height * 6144 + 
robot_x-movement * 1536 + robot_y-movement * 384 + lighting_intensity * 96 + 
lighting_y-dir * 16 + object_color * 4 + wall color

padded_index = index padded with zeros such that it has 6 digits.

To see the Isaac3D images by varying each factor of variation individually, you can run

python dataset_demo.py --dataset Isaac3D

and the results are saved in the examples/isaac3d_samples folder.

You can also check out the Isaac3D images here: isaac3d_samples_demo, which includes all the ground-truth latent traversals.

Links to datasets

The two datasets can be downloaded from Google Drive:

  • Falcor3D (98 GB): link
  • Isaac3D (190 GB): link

Besides, we also provide a downsampled version (resolution 128x128) of the two datasets:

  • Falcor3D_128x128 (3.7 GB): link
  • Isaac3D_128x128 (13 GB): link

License

This work is licensed under a Creative Commons Attribution 4.0 International License by NVIDIA Corporation (https://creativecommons.org/licenses/by/4.0/).

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Datasets for new state-of-the-art challenge in disentanglement learning

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