Datasets:

mipal
/

iPhone360-4dgs360

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/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/webdataset/webdataset.py", line 80, in _split_generators
                  raise ValueError(
                  ...<2 lines>...
                  )
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              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 68, 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.

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iPhone360 Dataset - 4dgs360 preprocessed version

iPhone360 is a benchmark dataset for 360° reconstruction of dynamic objects from monocular video, introduced in the paper:

4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video Jae Won Jang, Yeonjin Chang, Wonsik Shin, Juhwan Cho, Nojun Kwak Project Page · arXiv

Dataset Description

iPhone360 features real-world dynamic scenes captured with an iPhone, where test cameras are positioned at significantly different angles from training views. This enables evaluation of 360° reconstruction capabilities that existing datasets cannot provide.

Dataset Versions

This dataset is distributed in two versions:

iPhone360-4dgs360 (this folder) — includes all preprocessing outputs required to reproduce 4DGS360 training and evaluation end-to-end (2D/3D tracks, track-anything masks, refined depth/tracks from AnchorTAPIP3D, cached scene-normalization tensors, etc.). Large footprint.
iPhone360 — the same RGB/depth/mask/camera/points/splits data, with the 4DGS360-specific intermediate preprocessing outputs above excluded. Much smaller download.

If you're quickly adapting iPhone360 to a new paper/method, we recommend starting with iPhone360 version and evaluating on it first, rather than downloading the full iPhone360-4dgs360.

Scenes

Scene	Description
`block2`	Dynamic object scene
`goat`	Dynamic object scene
`jacket`	Dynamic object scene
`jelly`	Dynamic object scene
`pull-up`	Dynamic object scene
`walk-around`	Dynamic object scene

Data Structure

Each scene contains:

rgb/ — RGB frames
depth/ — Depth maps
masks/ — Object masks
camera/ — Camera parameters
splits/ — Train/test split definitions
points.npy — Initial point cloud
dataset.json / scene.json / metadata.json — Scene metadata
flow3d_preprocessed/ — Preprocessed optical flow data
video_depth_anything/ — Video depth estimates

Citation

If you use this dataset, please cite:

@article{jang2025_4dgs360,
  title     = {4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video},
  author    = {Jang, Jae Won and Chang, Yeonjin and Shin, Wonsik and Cho, Juhwan and Kwak, Nojun},
  journal   = {arXiv preprint arXiv:2603.21618},
  year      = {2025},
  url       = {https://arxiv.org/abs/2603.21618}
}

Download and extraction

The complete preprocessed dataset is distributed as 21 independent tar archives in the six scene folders at the repository root. Download every archive for each scene you need. Each archive preserves paths starting with its scene name; extract all archives into the same destination directory to restore the layout described above. These are independent tar files, not split parts of a single tar.

hf download mipal/iPhone360-4dgs360 --repo-type dataset --local-dir iphone360-download
cd iphone360-download
shasum -a 256 -c SHA256SUMS
mkdir -p ../iphone360-extracted
for archive in block2/*.tar goat/*.tar jacket/*.tar jelly/*.tar pull-up/*.tar walk-around/*.tar; do
  tar -xf "$archive" -C ../iphone360-extracted || exit 1
done

manifests/ contains each scene's original file paths, byte sizes, and modification timestamps. SHA256SUMS contains checksums of the tar archives.

Downloads last month: 2,425

Total file size:

96 GB

Paper for mipal/iPhone360-4dgs360

4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video

Paper • 2603.21618 • Published Mar 23 • 12