TianhangCheng7/IMU4DData

IMU4D Data

Processed motion / IMU training and evaluation data for IMU4D. The repo mirrors the data/processed/ tree of the IMU4D_dev code base, so downloading it into $IMU4D_DATA_ROOT/processed reproduces every default path used by the training configs.

Dataset	Path	train / val / test	Size
MotionMillion + LINGO	motionmillion/v1/wds/	913,308 / 58,444 / 170,757	~111 GB
HiPHI	hiphi/v1/{wds,splits}/	13,037 / 500 / 500	~13 GB
OMOMO	omomo/v1/{wds,splits,samples}/	5,279 / 500 / 500	~1.1 GB
HUMOTO	humoto/v1/{wds,splits,samples}/	685 / 50 / 50	~0.3 GB
Qwen3-0.6B caption rewrites	<dataset>/qwen3_0.6B_rewrite_v1/<split>/*.jsonl	one sidecar per shard	~1.5 GB
OMOMO canonical (upright) object meshes	omomo/canonical_objects/*.obj	11 assets	~15 MB
IMUPoser (real-world, measured IMUs)	imuposer/v2/{wds,splits,samples}/	133 / 34 / 34	~0.13 GB
NCSA phone / watch / earbud (real-world, PromptHMR pseudo-GT)	ncsa/v1/{wds,splits,samples}/ (T-pose-only calibration, default)	15 / 4 / 4 sessions	~17 MB
NCSA, oracle heading variant	ncsa/v1_heading_fix_by_visual_oracle/{wds,splits,samples}/	15 / 4 / 4 sessions	~17 MB

Real-world variants (details in dataset_process/realworld/README.md and dataset_process/ncsa/README_ncsa_imu.md of IMU4D_dev):

• imuposer/v2: world frame rotated z-up -> y-up, gravity-free acceleration (imu_acc_add_gravity = False). The uncorrected first conversion (v1) is not hosted any more; rebuild it from the legacy per-sequence pickles (data/raw/imuposer/legacy_v1, not hosted) with the plain converter if you need it.
• ncsa/v1: NCSA capture at 30 Hz (phone on the right thigh, watch on the left wrist, earbud on the right ear; slots 1 / 4 / 3, earbud copied to slot 2, slots 0 and 5 empty) as obtained from a T-pose-only calibration: acceleration sign fix and gravity-free acceleration (convert_ncsa_imu.py --acc-fix --no-loader-gravity), no heading fix. This is the default training / evaluation data.
• ncsa/v1_heading_fix_by_visual_oracle: the same readings with, in addition, a per-device yaw fix estimated against the PromptHMR pseudo-GT (--heading-fix). Oracle upper bound for diagnostics; select it with NCSA_IMU_ROOT=.../ncsa/v1_heading_fix_by_visual_oracle. The uncorrected handoff conversion is not hosted; rebuild it from the raw Box zip with convert_ncsa_imu.py without the fix flags.

Format

Every WDS root contains wds/manifest.json and wds/{train,val,test}/*.tar (WebDataset). Each sample is a pickle with motion_data_smpl85 (T, 85), imu_traj (T, 6, 6), texts, source, id; HiPHI / OMOMO / HUMOTO samples also carry object poses and identities. Real-world samples (IMUPoser, NCSA) store measured imu_acc (T, 6, 3) and imu_ori (T, 6, 3, 3) in model sensor order instead of imu_traj, plus imu_acc_add_gravity / imu_missing_slots flags read by the loader. Caption rewrites are JSONL sidecars keyed by sample id, one file per shard.

Download

pip install "huggingface_hub[cli]" webdataset
export IMU4D_DATA_ROOT=/path/to/IMU4D_dev/data

# everything (~130 GB)
hf download TianhangCheng7/IMU4DData --repo-type dataset \
  --local-dir "$IMU4D_DATA_ROOT/processed"

# a single dataset
hf download TianhangCheng7/IMU4DData --repo-type dataset \
  --local-dir "$IMU4D_DATA_ROOT/processed" --include "omomo/**"

# real-world fine-tune sets only (<0.5 GB)
hf download TianhangCheng7/IMU4DData --repo-type dataset \
  --local-dir "$IMU4D_DATA_ROOT/processed" --include "imuposer/**" --include "ncsa/**"

See the IMU4D_dev README (Datasets section) for how training and evaluation stream these shards and for the environment variables that override each root.

Not included

The original HiPHI / OMOMO / HUMOTO releases (data/raw/) are not redistributed; obtain them from the original authors. The legacy HuMoTo pickles (raw/humoto/v1/humoto_data/{all,all_time,*_indices.npy}, the rebuild source of humoto/v1) and the legacy IMUPoser / DIP-IMU per-sequence pickles (raw/{imuposer,dipimu}/legacy_v1, rebuild sources of the v2 sets) are part of data/raw/ and are not hosted either. DIP-IMU is not included either (its license does not permit redistribution): project members with a DIP-IMU license fetch dipimu/v2 from the private companion repo TianhangCheng7/IMU4DData_private, everyone else builds processed/dipimu/v2 from the official DIP_IMU_processed.zip as the IMU4D_dev README explains (dataset_process/realworld/build_corrected_variants.sh dipimu). The NCSA raw capture (data/raw/ncsa/: videos, PromptHMR fits) is not included either, only the converted IMU + SMPL-X clips. Intermediate build products (hiphi/v1/{smpl85,intermediate,samples}, previews, Rerun recordings) are omitted. The SMPL-X body model must be downloaded separately from https://smpl-x.is.tue.mpg.de under its own license.

Sources: MotionMillion, LINGO, HiPHI, OMOMO, HUMOTO, IMUPoser, DIP-IMU (not redistributed), NCSA phone / watch / earbud capture (UIUC NCSA, 2026-08-30 handoff; PromptHMR pseudo ground truth). Please follow each source dataset's license and citation requirements.