The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
Paint3D vs Hunyuan3D-2.0 Diverse De-lighting Benchmark
This repository contains a category-balanced benchmark for comparing how Paint3D and Hunyuan3D-2.0 handle image-conditioned 3D texturing when the input image contains visible illumination and shading.
Contents
- 50 Google Scanned Objects cases from 10 categories (5 cases per category).
- Strongly lit conditioning images and reference unlit albedo renders.
- 100 canonical method packages: 50 Paint3D and 50 Hunyuan3D-2.0 outputs.
- Two 360-degree GT videos per case: one with source lighting/shadows and one unlit.
- Per-case comparison images/videos, category contact sheets/videos, metrics, and an HTML browser.
The categories are footwear, bags_apparel, electronics,
tools_office, kitchen_appliances, toys_animals,
toy_transport_building, food_packaging, home_storage_decor, and
games_puzzles.
Repository layout
benchmark_inputs/
samples_all.txt
manifest.jsonl
input_contact_sheet.jpg
<case>/
input.png
reference_albedo.png
lighting_delta.png
model.obj
metadata.json
source/
canonical_outputs/
paint3d/<case>/
hunyuan3d/<case>/
visualization/
index.html
metrics.csv
category_summary.csv
<case>/comparison.png
<case>/comparison.mp4
Every directory under canonical_outputs/{paint3d,hunyuan3d}/<case> contains
exactly seven files:
input_image.png
input_mesh.obj
textured.glb
albedo_render.mp4
albedo_texture.png
gt_lit_render.mp4
gt_unlit_render.mp4
The method comparison videos are H.264/yuv420p, 3072 x 1024, 25 FPS, and 100 frames.
The two GT turntables are H.264/yuv420p, 768 x 768, 25 FPS, 100 frames, and 4 seconds.
All 100 canonical packages and all 200 GT file placements passed file-contract and
decoded-video validation. Reports are stored at
canonical_outputs/canonical_packaging_validation.json,
canonical_outputs/gt_validation_report.json, and
canonical_outputs/gt_placement_validation.json.
Summary metrics
Lower is better. Across all 50 cases:
| Metric | Paint3D | Hunyuan3D-2.0 |
|---|---|---|
| Front-view RGB MAE | 57.0272 | 61.5758 |
| Foreground mean-color RGB MAE | 27.9483 | 31.3628 |
| Per-case wins | 26 | 24 |
These metrics are proxy measurements and remain sensitive to geometry and view alignment. The comparison videos and contact sheets should be used for qualitative assessment of baked-in lighting and shadow removal.
Source and license
The source assets come from Google Scanned Objects:
- https://research.google/pubs/google-scanned-objects-a-high-quality-dataset-of-3d-scanned-household-items/
- https://github.com/kevinzakka/mujoco_scanned_objects
Google Scanned Objects is distributed under CC BY 4.0. Source attribution and
asset identifiers are retained in each case's metadata.json.
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