--- pretty_name: TopoRig license: other license_name: toporig-academic-use-1.0 license_link: LICENSE.md tags: - toporig - facial-animation - 3d - mesh - webdataset configs: - config_name: neutral_imgs default: true data_files: - split: data path: neutral_imgs/*.tar - config_name: expressed_imgs data_files: - split: data path: expressed_imgs/*.tar - config_name: meshes_ict data_files: - split: data path: meshes_ict/*.tar - config_name: meshes_custom data_files: - split: data path: meshes_custom/*.tar --- # TopoRig Published dataset: [SoroushMehraban/TopoRig_Dataset](https://huggingface.co/datasets/SoroushMehraban/TopoRig_Dataset). TopoRig contains neutral facial images, facial expressions labeled by action unit (AU), and rigged meshes in ICT and custom topologies for facial animation. ## Dataset contents | Collection | Records | Contents | | --- | ---: | --- | | `neutral_imgs` | 3,498 | One neutral PNG per identity | | `expressed_imgs` | 100,331 | Expressed PNGs for 3,466 identities, indexed by identity and AU | | `meshes_ict` | 3,497 | Rigged FBX meshes in ICT topology | | `meshes_custom` | 3,497 | Rigged FBX meshes in custom topology, with 3,496 landmark JSON files | All 103,829 images are 1024×1024 RGB PNGs. Meshes are stored once per identity and topology. The local dataset contains PNG, FBX and JSON files; downloadable releases can package these files as WebDataset tar shards. ## File organization ```text README.md metadata/ neutral_imgs/img_00006.png expressed_imgs/img_00006-26.png meshes_ict/img_00006.fbx meshes_custom/img_00006.fbx meshes_custom/img_00006.json ``` Match images and meshes using the complete identity string recorded in `metadata/manifest.jsonl`. Identify a mesh by both its collection and identity, since the two mesh collections can share the same filename. The two-digit expression suffix identifies a TopoRig AU in `0..52`. For example, `26` is `jawOpen`. Control IDs and names are listed in `metadata/action_units.json` and follow the TopoRig `AU_NAME` table. Landmark JSON files map MediaPipe landmark IDs to vertex indices in the matching FBX mesh. The training recipes specify Z up, +Y front and normalization to an extent of 2.0 when loading meshes. Stored FBX coordinates remain in their source coordinate system. ## Metadata and availability - `metadata/manifest.jsonl`: relative file paths, identities, collections, AUs, asset types, byte sizes and SHA-256 checksums. - `metadata/layout.json`: layout version, file counts and availability information. - `metadata/availability.jsonl`: the available collections and AUs for each identity. - `metadata/action_units.json`: AU IDs and names. - `metadata/bad_*.txt`: sample exclusions and their documented reasons. - `metadata/splits.json`: split availability and experiment selection settings. - `metadata/validation.json`: integrity and structural validation results. - `metadata/provenance.json`: available source information and distribution status. - `metadata/ict_split.csv`, `metadata/custom_split.csv`: original mesh assignments. - `metadata/ict_landmarks/`, `metadata/ict_eval_landmarks/`: 624 training and 350 benchmark mappings. - `metadata/evaluation_manifest.csv`: original 350 ICT + 350 custom benchmark. - `metadata/reproduction.json`: recovered-file checksums, coverage and validation limits. - `metadata/waft.json`: exact external WAFT version and weight acquisition records. Expression coverage varies by identity; 32 neutral identities have no expressed images. `img_00100` has neither mesh, and `img_02270` has no custom landmark JSON. ICT mappings for the recorded train/val and benchmark selections are included under `metadata/`. All nonexcluded training and validation identities and all 700 benchmark rows have the required meshes and mappings. Use the availability and exclusion metadata when selecting additional samples. ## Use with TopoRig Set `TOPORIG_DATA_ROOT` to the dataset directory and keep writable caches in a separate location using `TOPORIG_CACHE_ROOT`. ```python from dataset.mesh_dataset import MeshDataset dataset = MeshDataset( data_root="/path/to/TopoRig_assets", mesh_source="custom", asset_cache_dir="/path/to/asset_cache", cache_dir="/path/to/tensor_cache", split="all", action_units=[26], blendshape_name_mapping="canonical", mesh_up_axis="z", mesh_front_axis="y", exclude_mesh_ids=["img_02270"], ) ``` ```python from dataset.image_dataset import ImageDataset dataset = ImageDataset( data_root="/path/to/TopoRig_assets", asset_cache_dir="/path/to/asset_cache", cache_dir="/path/to/tensor_cache", split="all", action_units=[8, 9, 10, 11, 20, 21, 22, 23], exclude_person_ids=["img_00100", "img_02270"], use_mediapipe_landmarks=True, require_mediapipe_landmarks=True, mesh_up_axis="z", mesh_front_axis="y", ) ``` These examples require the TopoRig code and its relevant runtime dependencies. The supplied `reference_assets/` directory includes the demo meshes and oral assemblies. Blender/FBX support, MediaPipe, and WAFT source and weights are separate dependencies. Follow the TopoRig preparation instructions for derived landmarks, mesh tensors and supervision caches. ## Downloadable shard format A packaged release contains `toporig_manifest.json` and tar shards for each collection: ```text neutral_imgs/shard-00000.tar expressed_imgs/shard-00000.tar meshes_ict/shard-00000.tar meshes_custom/shard-00000.tar ``` The release manifest records archive members, materialized file paths, identities, AUs, sizes and checksums. Each mesh record includes a `landmarks.bin` field with its JSON mapping, or empty bytes when unavailable; `has_landmarks` identifies which records include a mapping. Shards target 512 MiB while keeping each record together. Install the TopoRig code and its `hub` dependencies, then download this release including the recovered metadata supplement: ```bash python -m dataset.hf_assets \ --repo-id SoroushMehraban/TopoRig_Dataset \ --revision main \ --local-dir /path/to/TopoRig_download ``` Use `--components meshes_ict meshes_custom` to download selected collections. Pass the downloaded directory as `data_root` in the examples above. TopoRig extracts selected files into a separate asset cache and verifies their checksums. Keep downloaded assets read-only and allocate separate space for extracted files, derived caches and outputs. ## Splits and experiment reproduction The shard split named `data` represents the complete inventory. Original train/val/test assignments and benchmark membership are provided in `metadata/`. After recorded exclusions, training selects 499 ICT and 2,796 custom heads; validation selects 63 ICT and 350 custom heads. The separate benchmark has 350 heads from each source. Preserve the supplied CSVs, AU settings and exclusions. The original payload commit `fd4f1f9f1996c139db8bf0ff85b3048f6e250d61` lacks this metadata supplement. Download the updated release and record its resolved commit for reproducibility. All 228 original shards are unchanged. Data inputs for both training stages, validation and the benchmark are covered. Users also need an accessible TopoRig code release, Blender/MediaPipe and the external WAFT dependencies documented in [REPRODUCING.md](REPRODUCING.md). Derived caches must be prepared; a complete GPU training/evaluation run has not been performed as part of this metadata audit. ## Provenance and use terms The dataset is free to use, modify, and share for noncommercial academic research, education, and teaching under the [TopoRig Academic Use License 1.0](LICENSE.md), with attribution. Commercial use requires separate permission. Underlying third-party rights remain with their respective holders. Cite TopoRig, the dataset repository URL, and the exact revision used. A paper citation and complete generation-source records are not included in this release. The original provenance audit is retained in `metadata/provenance.json`. The checkpoint directory `checkpoints_main_and_ablations_20260908` is excluded from the dataset release. See [REPRODUCING.md](REPRODUCING.md) in the packaged release for download, preparation, training, and evaluation commands. ## Release package This release contains 228 tar shards (112.03 GiB), plus metadata and reference assets. All 114,319 original payload files were checksum-verified while packaging. Shard counts by collection: - `neutral_imgs`: 3 shards - `expressed_imgs`: 73 shards - `meshes_ict`: 48 shards - `meshes_custom`: 104 shards See `metadata/packaging_validation.json` for the build record and [REPRODUCING.md](REPRODUCING.md) for the complete training/evaluation procedure.