| --- |
| license: other |
| license_name: release-license-pending |
| license_link: LICENSE.md |
| tags: |
| - pointzero |
| - 3d |
| - robotics |
| - point-tracking |
| - synthetic |
| - webdataset |
| pretty_name: PointZero Sparse Synthetic Pretraining |
| configs: |
| - config_name: preview |
| default: true |
| data_files: |
| - split: preview |
| path: previews/representative.parquet |
| - config_name: shorts_v7 |
| data_files: |
| - split: train |
| path: data/shorts_v7/train/shard-*.tar |
| - split: test |
| path: data/shorts_v7/test/shard-*.tar |
| - config_name: tshirt_v7 |
| data_files: |
| - split: train |
| path: data/tshirt_v7/train/shard-*.tar |
| - split: test |
| path: data/tshirt_v7/test/shard-*.tar |
| - config_name: towel_v7 |
| data_files: |
| - split: train |
| path: data/towel_v7/train/shard-*.tar |
| - split: test |
| path: data/towel_v7/test/shard-*.tar |
| - config_name: genesis_partnet_50k |
| data_files: |
| - split: train |
| path: data/genesis_partnet_50k/train/shard-*.tar |
| - split: test_similar |
| path: data/genesis_partnet_50k/test_similar/shard-*.tar |
| - split: test_unseen |
| path: data/genesis_partnet_50k/test_unseen/shard-*.tar |
| - config_name: kubric_50k_v2 |
| data_files: |
| - split: train |
| path: data/kubric_50k_v2/train/shard-*.tar |
| - split: test |
| path: data/kubric_50k_v2/test/shard-*.tar |
| - config_name: trajectory_index |
| data_files: |
| - split: all |
| path: index/*/*/shard-*.parquet |
| --- |
| |
| # PointZero Sparse Synthetic Pretraining |
|
|
| Sparse, training-ready synthetic trajectories associated with |
| [PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics](https://arxiv.org/abs/2609.19142). |
|
|
| **Release candidate: uploading and validation in progress.** This repository is |
| private staging until its license and the full release checks are finalized. |
| Check `release-status.json` for committed shard counts. The table below describes |
| the frozen intended source inventory, not a claim that every shard is uploaded. |
|
|
| This release covers synthetic pretraining and its original synthetic evaluation |
| splits only. It contains 290,097 intended training trajectories |
| (2,900,970 frames), and 322,230 trajectories including evaluation. |
| Real-world recordings, robot demonstrations, model predictions, and checkpoints |
| are outside this release. |
|
|
| The default viewer shows 33 representative thumbnails from the validated pilot. |
| These previews are separate from the full training/evaluation shards. |
|
|
| ## Contents and original splits |
|
|
| | Component | Split | Source scene entries | Trajectories | Frames | |
| |---|---|---:|---:|---:| |
| | shorts_v7 | train | 6,885 | 68,712 | 687,120 | |
| | shorts_v7 | test | 20 | 200 | 2,000 | |
| | tshirt_v7 | train | 6,593 | 65,788 | 657,880 | |
| | tshirt_v7 | test | 20 | 200 | 2,000 | |
| | towel_v7 | train | 5,426 | 54,190 | 541,900 | |
| | towel_v7 | test | 20 | 199 | 1,990 | |
| | genesis_partnet_50k | train | 5,058 | 50,576 | 505,760 | |
| | genesis_partnet_50k | test_similar | 1,600 | 16,000 | 160,000 | |
| | genesis_partnet_50k | test_unseen | 1,534 | 15,339 | 153,390 | |
| | kubric_50k_v2 | train | 5,179 | 50,831 | 508,310 | |
| | kubric_50k_v2 | test | 20 | 195 | 1,950 | |
|
|
| A scene may contain multiple context trajectories. These are different counting |
| units. Original scene split manifests are preserved under `splits/`, with SHA256 |
| digests in `manifests/source-inventory.json`. PartNet's `test_similar` and |
| `test_unseen` are separate splits. |
|
|
| ## Shard format |
|
|
| Each approximately 1 GB uncompressed TAR is a WebDataset shard. Members for one |
| trajectory are adjacent and share a stable key: |
|
|
| ```text |
| data/shorts_v7/train/shard-000000.tar |
| shorts_v7__000603__0000.points.npy |
| shorts_v7__000603__0000.graspers.npy |
| shorts_v7__000603__0000.rgb_000.png |
| shorts_v7__000603__0000.segmentation_000.png |
| ... remaining RGB and segmentation frames ... |
| shorts_v7__000603__0000.json |
| ``` |
|
|
| | Field | Meaning | |
| |---|---| |
| | `points.npy` | float32 array `[T<=10, N<=1000, XYZ=3]`; point index correspondence is preserved across time | |
| | `graspers.npy` | optional float32 array `[G, T<=10, XYZ=3]` containing recorded interaction tracks | |
| | `rgb_000.png` ... `rgb_009.png` | exact original PNG bytes for all ten timesteps | |
| | `segmentation_000.png` ... `segmentation_009.png` | exact original segmentation PNG bytes | |
| | `json` | component, split, source scene/context IDs, timestep names, image dimensions, sampling method, and hashes | |
|
|
| The arrays preserve source training coordinates and float32 precision. The export |
| does not normalize, transform, resample, or regenerate point tracks. This sparse |
| release does not include original dense geometry, depth maps, camera calibration, |
| or per-frame visibility labels. `first_step` names describe the existing sparse |
| cache convention; they are not newly computed visibility annotations. |
|
|
| Rigid trajectories use the existing first-step FPS cache; the other components |
| use the existing first-step sparse cache. Articulated and rigid trajectories |
| normally have no recorded grasper array: PointZero generates conditioning tracks |
| from moving points during batch preparation. |
|
|
| Some rigid trajectories contain fewer than 1000 points in their existing FPS cache. |
| The export preserves those counts; batching uses the existing PointZero padding behavior. |
|
|
| ## Short source sequences |
|
|
| Some source cloth trajectories contain only 2–8 frames. Their available points, |
| interaction tracks, and images are preserved exactly and their metadata records |
| `quality.default_training_eligible: false`. The `frames` column of each Parquet |
| index records the actual frame count. The supplied adapter excludes sequences |
| shorter than ten frames from default training; setting `training=False` retains |
| all available sequences for inspection. No missing frames are synthesized. |
|
|
| Until the full audit completes, frame totals in the inventory table are nominal |
| counts at ten frames per trajectory. The final validation report will record |
| actual frame totals and the number of short source sequences. |
|
|
| ## Download one component and train without extraction |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| root = snapshot_download( |
| repo_id="bartduis/pointzero-synthetic", |
| repo_type="dataset", |
| allow_patterns=[ |
| "data/shorts_v7/train/*.tar", |
| "manifests/shards/shorts_v7/train/*.json", |
| "pointzero_shards.py", |
| ], |
| # Set revision to the published immutable release commit for reproducibility. |
| ) |
| |
| import sys |
| sys.path.insert(0, root) |
| from pointzero_shards import PointZeroShardDataset |
| from torch.utils.data import DataLoader |
| |
| dataset = PointZeroShardDataset(root, components=["shorts_v7"], epoch_samples=10000) |
| # Use PointZero's existing utils.collate.collate_pad for mixed grasper counts. |
| from utils.collate import collate_pad |
| loader = DataLoader(dataset, batch_size=8, num_workers=4, collate_fn=collate_pad) |
| for epoch in range(10): |
| dataset.set_epoch(epoch) |
| for batch in loader: |
| points = batch["current_scene"]["points"] |
| # Pass the batch through the existing PointZero batch preparation/model. |
| ``` |
|
|
| `epoch_samples` is per distributed rank. Do not attach a DistributedSampler: |
| the iterable adapter handles distributed/worker iteration. Training intentionally |
| samples with replacement, with independently seeded worker streams; shuffle order |
| differs from the original directory loader. Evaluation partitions shards across |
| ranks and workers and traverses every sample once. Set `training=False` for exact |
| traversal, including when inspecting a train split. |
|
|
| The adapter returns PointZero's `current_scene` and `context` structure. RGB and |
| segmentation conditioning use the first timestep, matching the inspected loader; |
| all other images remain available in the archive. The dataset supports standard |
| WebDataset readers too. Parquet indexes provide each sample's shard, TAR byte |
| offset, and byte length for local random access. |
|
|
| ## Component sampling |
|
|
| The adapter's default weights reproduce the inspected runnable configuration: |
| shorts/T-shirt/towel each 0.33, articulated 1.0, rigid 0.33, normalized across |
| selected components. These rounded weights differ from the paper's stated |
| deformable/articulated/rigid mixture of 4/9, 4/9, 1/9. To use that stated mixture: |
|
|
| ```python |
| paper_weights = { |
| "shorts_v7": 4/27, "tshirt_v7": 4/27, "towel_v7": 4/27, |
| "genesis_partnet_50k": 4/9, "kubric_50k_v2": 1/9, |
| } |
| dataset = PointZeroShardDataset(root, weights=paper_weights) |
| ``` |
|
|
| Download every selected component's train shards before mixing components. |
|
|
| ## Provenance, license, and limitations |
|
|
| Deformable interactions use procedural clothing with FleX physics and Blender |
| rendering. Articulated interactions use PartNet-Mobility objects in Genesis. |
| Rigid interactions use modified Kubric scenes. See the paper for generation |
| details. This is simulation data with sparse surface correspondences; it does |
| not represent the full diversity or realism of physical interactions. |
|
|
| The release license is pending. No blanket license is assigned to third-party |
| assets by this staging repository. PartNet-Mobility has noncommercial research |
| and education terms, recipient-agreement requirements, and upstream ShapeNet |
| terms; see [the official dataset](https://huggingface.co/datasets/sapien-sim/PartNetMobility). |
| See `LICENSE.md` and `ATTRIBUTION.md` for the component-specific release record. |
|
|
| ## Validation and versioning |
|
|
| The exporter checks 1–10 available frames per trajectory, finite float32 coordinates, |
| consistent grasper counts, and matching RGB/segmentation dimensions. Only the |
| explicit source-file allowlist is included. Every released member has a SHA256 |
| digest in its sample metadata, and each shard/index has a digest in |
| `manifests/shards/`. A pilot validated exact source equivalence, PNG integrity, |
| indexed access, weighted training, and disjoint distributed evaluation. |
|
|
| The final release will record exact exported counts, any exclusions, and an |
| immutable Hugging Face revision. Source files are left unchanged. |
|
|
| ## Citation |
|
|
| Please cite [PointZero](https://arxiv.org/abs/2609.19142) and the upstream dataset |
| and generation resources relevant to the components you use. A final BibTeX |
| entry will be included with the completed release. |
|
|
