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README.md

@@ -34,10 +34,28 @@ Full profiling details: [`tools/taco_analysis/profile_comparison.md`](tools/taco
 * **12** allocentric cameras per sequence at **512x376**
 * 2D segmentation masks at **375x512**
 * Egocentric RGB-D videos (original resolution)
-* Hand-object pose annotations
+* Hand-object pose annotations + pre-computed 3D hand joints
 * **206** high-resolution object models
 * Camera parameters (intrinsics rescaled)
 
+### Archive Contents
+
+| Archive | Size | Contents |
+| --- | ---: | --- |
+| `Marker_Removed_Allocentric_RGB_Videos.zip` | 4.3 GB | 12 camera MP4s per sequence (512x376) |
+| `2D_Segmentation.zip` | 145 GB | Per-camera segmentation masks (375x512 npy) |
+| `Hand_Poses.zip` | 25.9 GB | MANO params (pkl) + pre-computed 3D joints (npy) |
+| `Hand_Poses_3D.zip` | 160 MB | **3D joints only** — `hand_joints.npy` per sequence `(T, 2, 21, 3)` float32 |
+| `Object_Poses.zip` | 64 MB | Object 6DoF transforms (npy) |
+| `Egocentric_RGB_Videos.zip` | 641 MB | Egocentric RGB videos |
+| `Egocentric_Depth_Videos.zip.*` | 959 MB | Egocentric depth videos (split archive) |
+| `object_models_released.zip` | ~1.2 GB | 206 high-res object meshes |
+| `mano_v1_2.zip` | small | MANO hand model files |
+
+`Allocentric_Camera_Parameters/` and `taco_info.csv` are stored directly (not zipped).
+
+**Tip:** If you only need 3D hand joints (not raw MANO parameters), download `Hand_Poses_3D.zip` (160 MB) instead of `Hand_Poses.zip` (26 GB).
+
 ### Downloading
 
 ```bash
@@ -52,16 +70,32 @@ cd taco_dataset_resized
 for z in *.zip; do unzip -qn "$z"; done
 ```
 
+```bash
+# Minimal download (allocentric videos + cameras + 3D hand joints only)
+huggingface-cli download mzhobro/taco_dataset_resized \
+    --repo-type dataset \
+    --include "*.csv" "*.sh" "*.md" \
+              "Marker_Removed_Allocentric_RGB_Videos.zip" \
+              "Allocentric_Camera_Parameters/**" \
+              "Hand_Poses_3D.zip" \
+    --local-dir taco_dataset_resized
+```
+
+### Hand Poses 3D Format
+
+`Hand_Poses_3D/{action}/{sequence_id}/hand_joints.npy` — shape `(T, 2, 21, 3)`:
+- **T**: number of frames
+- **Dim 1**: 0=left hand, 1=right hand
+- **21 joints**: wrist, index(MCP,PIP,DIP,tip), middle(...), ring(...), pinky(...), thumb(CMC,MCP,IP,tip)
+- **3**: xyz world coordinates in meters
+
 ```python
-from huggingface_hub import snapshot_download
-
-snapshot_download(
-    "mzhobro/taco_dataset_resized",
-    repo_type="dataset",
-    local_dir="taco_dataset_resized",
-    max_workers=1,
-  )
+# Loading in the dataset loader
+ds = TACODataset(..., load_hand_joints=True)
+sample = ds[0]
+sample["hand_joints"]  # (T, 2, 21, 3) float32
 ```
+
 ### Related
 
 - **Original full-resolution dataset**: [mzhobro/taco_dataset](https://huggingface.co/datasets/mzhobro/taco_dataset)
@@ -72,6 +106,7 @@ The `tools/` directory contains:
 - `taco_dataset_loader.py` — PyTorch Dataset class for loading TACO data
 - `view_sampler.py` — Camera view sampling strategies
 - `generate_taco_csv.py` — Generate `taco_info.csv` metadata
+- `precompute_hand_joints.py` — Pre-compute 3D hand joints from MANO parameters
 - `taco_analysis/` — Analysis and visualization scripts (dataset stats, camera extrinsics, epipolar lines, mesh overlays, profiling)
 - `resizing_pipeline/` — Scripts used to produce this resized dataset from the original