What Physics do Data-Driven MoCap-to-Radar Models Learn?
Paper • 2605.00018 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowTypeError
Message: ("Expected bytes, got a 'float' object", 'Conversion failed for column None with type object')
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 199, in _generate_tables
pa_table = pa.Table.from_pandas(df)
File "pyarrow/table.pxi", line 4796, in pyarrow.lib.Table.from_pandas
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 651, in dataframe_to_arrays
arrays = [convert_column(c, f)
~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 639, in convert_column
raise e
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 633, in convert_column
result = pa.array(col, type=type_, from_pandas=True, safe=safe)
File "pyarrow/array.pxi", line 365, in pyarrow.lib.array
result = _ndarray_to_array(values, mask, type, c_from_pandas, safe,
File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
check_status(NdarrayToArrow(pool, values, mask, from_pandas,
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'float' object", 'Conversion failed for column None with type object')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.
MoCap2Radar v3
Synchronised optical motion-capture (53 markers, 240 Hz) and 5.8 GHz radar I/Q (256 Hz) recordings of a walking subject. Used in What Physics do Data-Driven MoCap-to-Radar Models Learn? (Kevin Chen, Kenneth W. Parker, Anish Arora; 2026 IEEE Radar Conference, RadarConf26; arXiv:2605.00018).
Recordings
| recording | split | duration (s) | windows (256 / hop 32) |
|---|---|---|---|
| DiagonalLong1 | train | 118.6 | 942 |
| DiagonalLong1Fast | train | 60.4 | 476 |
| DiagonalLong2 | train | 103.7 | 822 |
| DiagonalLong2Fast | train | 54.9 | 432 |
| DiagonalLong3 | train | 117.1 | 930 |
| DiagonalLong3Fast | train | 71.3 | 563 |
| DiagonalLong4 | val | 119.8 | 951 |
| DiagonalLong4Fast | val | 81.1 | 641 |
| RandomWalk1 | eval | 303.0 | 2416 |
| RandomWalk2 | eval | 301.4 | 2404 |
Files
raw/v3/mocap/<recording>.csv— 53 markers × (x, y, z) in mm, world frame of the capture volume, 240 Hz. Vicon export: first row is the subject label (Subject1_9_20, anonymised), second row the column headers (Subject1_9_20\<MARKER><T-X|Y|Z>). Rows after theRadar9_20sentinel are a second Vicon subject, the four markers on the radar board, and are dropped by the loader.raw/v3/radar/<recording>.csv— first column is a beacon column (dropped); remaining columns are I/Q samples at 256 Hz.config.yaml— STFT parameters (nperseg 256, noverlap 224), 50 ms startup delay, mocap sample rate, and the split definition above.scalers.npz—StandardScalermean / scale fitted on the train split only:mocap[159],mocap_local[159],mocap_doppler[53],radar_sxx[1],radar_iq_centered[2]. Stored as plain arrays (<name>_mean,<name>_scale).SHA256SUMS— checksums of every file above.
Preprocessing
Regenerate the model-ready arrays with python src/preproc.py from the code
repository (). The pipeline: load radar I/Q and mocap, apply the startup
delay, trim both streams to a common duration, upsample mocap to 256 Hz, convert
mocap to radar-relative coordinates (radar centroid at the origin), window with
nperseg 256 / hop 32, compute the two-sided complex STFT of the radar I/Q and take
magnitude in dB, fit the scalers on the train split and apply them to all splits.
scalers.npz lets you skip the fitting step and check your regeneration against it.
Consent and ethics
Citation
@inproceedings{chen2026whatphysics,
title = {What Physics do Data-Driven MoCap-to-Radar Models Learn?},
author = {Chen, Kevin and Parker, Kenneth W. and Arora, Anish},
booktitle = {2026 IEEE Radar Conference (RadarConf26)},
year = {2026},
url = {https://arxiv.org/abs/2605.00018}
}
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