Vastextures: Vast repository of textures and PBR materials extracted from real-world images using unsupervised methods
Paper • 2406.17146 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: UnidentifiedImageError
Message: cannot identify image file <_io.BytesIO object at 0x7f9006f0aa20>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2159, in _apply_feature_types_on_example
decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2204, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 190, in decode_example
image = PIL.Image.open(bytes_)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3498, in open
raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f9006f0aa20>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
General
VasTexture is a large-scale dataset of textures and PBR materials extracted from real-world images. The repository contains 500,000 highly diverse texture images and PBR materials. All assets are free to download and use for any purpose (CC0 license). The dataset is divided into textures images, and PBR materials. Where texture image are simply crop of regions in images with uniform textures.
The PBR materials and textures were extracted from natural images using an unsupervised statistical approach (no human intervention). As a result, the textures and PBR materials are significantly more diverse but less refined compared to assets made using manual and AI approaches. This dataset is more suitable for tasks needing a large number of highly diverse assets like building datasets or large scale procedural generation.
Project Website
File Structure
The dataset is composed into two assets types textures images and PBR materials.
Texture image files contain the world Texture in the file. PBR materiasl files contain the world PBR in the file. If the PBR are seamless/tilable, the world seamless will appear in the file name (note that for textures images this mean some modification was done on texture edges).
If the PBR is 512x512 or larger, the world large will appear in the file name. Most files will have texture size in their name.
Each file contain between 1,000 to 40,000 assets. Files with the the word sample contain few dozens to few hundered samples.
Data generation code:
The Python scripts used to extract these assets are supplied at:
Texture_And_Material_ExtractionCode_And_Documentation.zip
The code could be run in any folder of random images extract regions with uniform textures and turn these into PBR materials.
Code for transforming data to seamless available at https://github.com/sagieppel/convert-image-into-seamless-tileable-texture
GITHUB and Alternative download sources:
GitHub: Texture/PBR extraction, Texture To Seamless
https://sites.google.com/view/infinitexture/home
https://zenodo.org/records/12629301
Papers
Main paper: Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation
More detailed: Vastextures: Vast repository of textures and PBR materials extracted from real-world images using unsupervised methods
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