yuzhench/gigahands-vitra-mano

GigaHands → VITRA Stage-1, official-MANO annotations

VITRA Stage-1 hand annotations for GigaHands, with all joint positions taken from GigaHands' official MANO fit instead of mixing in triangulated keypoints. Annotations only — no videos (get those from GigaHands; the mapping is described in §5).

episodes	13,247 (train 11,904 / test 1,343)
frames	3,395,733
camera	brics-odroid-001_cam0 (static rig; one constant extrinsic per scene)
source	GigaHands params/ + keypoints_3d_mano/ + optim_params.txt

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1. Three differences from the usual conversion

VITRA's converter by default takes joints from keypoints_3d (multi-view triangulation) while taking wrist 6DoF from params (the MANO fit). This dataset takes everything from the official MANO fit. Three changes, each with measurements.

(a) joints_worldspace: triangulation → official MANO

Joints are taken directly from the official keypoints_3d_mano; not a single coordinate is recomputed. That file has its per-frame centroid zeroed out (measured: per-frame centroid std = 0), so only the translation is missing. It is restored as:

wrist_world(t) = R(Rh_t) · j0 + Th_t          # official params + the constant j0 only
c(t)           = wrist_world(t) − wrist in the official points(t)
joints_world(t) = official points(t) + c(t)

j0 is the model-space wrist. It depends only on beta, not on finger pose (measured: per-frame std = 0, ‖j0‖ ≈ 0.096 m), so it is computed once per sequence. This is the only quantity we compute ourselves.

The official file zeroes the centroid over both hands' 42 points jointly, so c(t) is shared between hands. This conversion solves it from the right hand and applies it to both — solving it independently for the left hand requires approximating its j0 with a mirrored right-hand model, which we measured to deviate by 1.75–2.96 cm (max 11.6 cm) from an independent pipeline, versus 0.25–0.29 cm for the right hand.

Why the change — hand-size stability of the two annotation sources (sum of 20 bone lengths, right hand, first frame, metres):

scene	official MANO	triangulated
p001-packing	0.772	1.104
p002-dog	0.783	0.995
p002-firstaid	0.786	0.971
p002-packing	0.774	0.797
p003-instrument	0.784	0.869
p003-packing	0.777	0.905
spread	1.8%	38.5%

p002-dog (0.995) and p002-packing (0.797) are the same participant; the triangulated side differs by 25% between them.

(b) transl_worldspace: Th → wrist

We store joints_worldspace[:, 0] (the wrist), not MANO's Th. The two differ by a constant 9.6 cm. Rationale: VITRA's other domains store the wrist (data/tools/hand_recon_core.py:118, transl_new = wrist + transl).

In this dataset transl_worldspace == joints_worldspace[:,0] holds exactly, frame by frame (sampled max difference 0.0e+00).

(c) hand_pose: add hands_mean

We store matrix(poses[3:] + hands_mean). GigaHands' poses are relative to the MANO mean hand, whereas VITRA's other domains store HaWoR's absolute rotation matrices (MANOLayer runs with pose2rot=False, so the full_pose += pose_mean at smplx/body_models.py:184 never executes). Adding it back puts both on the same convention.

Test — how well the result reproduces the official keypoints_3d_mano (6 scenes):

variant	mean error
without (flat_hand_mean=True)	3.39 cm
with hands_mean	1.29 cm

⚠ The GigaHands paper states "PCA disabled + flat mean shape", which disagrees with this measurement. This dataset follows the measurement.

Global-transform convention (determined by measurement, not assumed)

Using keypoints_3d as a yardstick, one sequence from each of 6 scenes, mean per-joint Euclidean distance:

convention	left hand	left error	right error
v_world = R(Rh)·v_model + Th (about the model origin)	mirrored	6.91 cm	6.71 cm
v_world = R·(v−j0) + j0 + Th (about the root joint)	mirrored	16.41 cm	15.52 cm
about the model origin	not mirrored	10.32 cm	6.71 cm
about the root joint	not mirrored	18.34 cm	15.52 cm

The left hand uses the right-hand model with the x-axis flipped, matching VITRA (hand_recon_core.py:112-115); MANO_LEFT.pkl is never loaded.

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2. ⚠ Known issue: 453 episodes we believe are image↔label misaligned (please verify independently)

residual_misaligned_episodes.json lists 453 / 13,247 (3.42%) episodes whose annotation length differs from the frame count of the video they are bound to by more than 12 frames:

GigaHands_p001-packing_000_...   params 381   video 483   mano 381
GigaHands_p001-packing_043_...   params 583   video 702   mano 583
GigaHands_p002-packing_002_...   params 177   video 336   mano 177

The pattern is consistent: params and keypoints_3d_mano always agree with each other, and differ from the video by 15–159 frames — not the small difference you would expect from tail trimming.

Our reading is that the annotated time span does not correspond to the video, so the hand at frame t and the image at frame t are not the same instant. This is only our reading. There may be an alignment scheme we have not considered (some timestamp mapping, for instance). These episodes are still included in the dataset — please check them yourself and decide whether to drop them. If you find that this is not actually a problem, or you know the correct alignment, we would very much like to hear it.

For contrast: episodes where params length ≠ keypoints_3d_mano length: 0 / 13,247 (0.00%).

A second, unquantified problem

A few episodes have all three lengths agreeing, yet projection shows the annotation does not match the video content. Example: p001-packing/023 (params 225 / keypoints 225 / video 232) — projecting its annotation onto that video puts the skeleton nowhere near the hands; shifting the video index by ±2 does not help; projecting the triangulated keypoints instead fails identically. This class cannot be detected by length checks. Its size is unknown and it has not been removed.

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3. What was excluded

criterion	count	removed?
low_valid_ratio < 0.9 (too few valid triangulated frames)	209	✅
missing keypoints_3d_mano (p017-fastfood/049)	1	✅
clip too short (min_frames=32)	1	✅
the 453 suspected misalignments above	453	❌ kept — see §2

About those 209: the new pipeline uses MANO-fit output, whose values are always finite, so the low_valid_ratio check always passes under it (our raw run reported skipped_low_valid_ratio = 0). That is not the data getting better — it is the check going blind. Frames where triangulation failed get filled in by the MANO fit with plausible-looking poses, which is harder to notice. We therefore reused the same exclusion list derived from triangulation validity.

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4. Contents and format

Annotation/
├── gigahands_real_train/
│   ├── episodic_annotations.tar.zst      # 11,904 .npy files
│   ├── episode_frame_index.npz           # 3,127,323 (episode_idx, frame_idx) pairs
│   └── conversion_report.json
├── gigahands_real_test/
│   ├── episodic_annotations.tar.zst      # 1,343 .npy files
│   ├── episode_frame_index.npz
│   └── conversion_report.json
└── statistics/
    ├── gigahands_real_train_keypoints_statistics.json
    └── gigahands_real_test_keypoints_statistics.json
subset_manifest.json                       # 13,249 clips (includes the 2 that were dropped)
residual_misaligned_episodes.json          # the 453 from §2

Each .npy is a dict; left and right each contain:

field	shape	notes
beta	(10,)	MANO shape, from the official shapes
global_orient_worldspace	(T,3,3)	matrix(Rh)
global_orient_camspace	(T,3,3)	left-multiplied by R_w2c
hand_pose	(T,15,3,3)	matrix(poses[3:] + hands_mean)
transl_worldspace	(T,3)	wrist, = joints_worldspace[:,0]
transl_camspace	(T,3)
joints_worldspace	(T,21,3)	official MANO joints + restored translation, OpenPose order (wrist = 0)
joints_camspace	(T,21,3)
kept_frames	(T,) bool

Also present: text / text_rephrase (language instruction), video_name, video_decode_frame, intrinsics, extrinsics, camera.

16-frame windows are cut at training time; they are not the storage granularity. Each .npy holds a whole segment (length 32–8996, median 175); episode_frame_index.npz enumerates every frame as a possible window start.

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5. How this maps to the videos (videos are not in this repo)

Get the videos from GigaHands. The episode filename encodes everything needed to locate them:

GigaHands_<scene>_<sequence_id>_<camera>_<video_stem>_f<start:06d>_<end:06d>_ep_000000.npy
           │       │             │        │            │        └─ end frame in the source video (exclusive)
           │       │             │        │            └─ start frame
           │       │             │        └─ video filename without .mp4
           │       │             └─ brics-odroid-001_cam0
           │       └─ GigaHands sequence id
           └─ GigaHands scene name

The video lives at multiview_rgb_vids/<scene>/<camera>/<video_stem>.mp4. Each clip in subset_manifest.json also records video_path / start_frame / end_frame directly.

⚠ Sequence ids do not correspond one-to-one with the sorted position of video files in a directory — sequence names have gaps (e.g. p003-instrument has no 023, so '024' sits at sorted index 23). Locate videos by the video_stem in the filename, never by index.

Camera intrinsics and extrinsics are in each .npy under intrinsics / extrinsics, taken from the brics-odroid-001_cam0 row of GigaHands' official optim_params.txt. This is a static rig: one constant extrinsic per scene (the converter replicates it across frames), so no SLAM is needed and no per-frame alignment is required within a window.

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6. What was not done (please do not infer quality from this)

• No A/B training comparison was run. "Official-MANO is better than triangulated" is currently supported only by indirect evidence (bone-length spread 1.8% vs 38.5%; normalization std about 33× smaller) — there is no training result behind it.
• The normalizer changed accordingly. The statistics here are not interchangeable with those of a triangulated-joint version; MSE values across different normalizers are not directly comparable and must be de-normalized to MANO physical units first.
• The second problem in §2 (all lengths agree but content does not) is unquantified and not removed.

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Citation and licence

This repository contains derived annotations of GigaHands. The original data, videos and MANO fit parameters remain the property of the GigaHands authors; please follow the GigaHands licence and cite their paper. The format follows VITRA (microsoft/VITRA) Stage-1.

The MANO model itself is under the MPI licence and must be obtained from https://mano.is.tue.mpg.de — no MANO model file is included here.