Cleaner card, quick start 0-4

README.md

@@ -23,21 +23,25 @@ configs:
     path: manifest_test.csv
 ---
 
+<div align="center">
+
 # ForceBody
 
-**Force-Paired Parametric Body Motion with Torque Uncertainty** (NeurIPS 2026, Evaluations & Datasets Track)
+**Force-Paired Parametric Body Motion with Torque Uncertainty**
+
+NeurIPS 2026, Evaluations & Datasets Track
 
-## 👉 Code, loader and benchmark: [github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026](https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026)
+### [💻 Code and benchmark on GitHub](https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026)
 
-This page holds the data only. Release 1.3.0 (2026-10-29).
+</div>
 
 <p align="center"><img src="assets/teaser.png" width="100%"></p>
 
-SKEL body motion paired with measured ground reaction forces (GRF), inverse-dynamics joint torques and a per-frame, per-joint torque uncertainty σ<sub>τ</sub>.
+ForceBody pairs SKEL body motion with measured ground reaction forces (GRF), inverse-dynamics joint torques, and a per-frame, per-joint torque uncertainty σ<sub>τ</sub>. This page holds the data. Code, loader and benchmark are on GitHub.
 
-| Trials | Subjects | Hours | Frames | Sources |
-|:---:|:---:|:---:|:---:|:---:|
-| 10,386 | 140 | 27 | 9.7M | 10 studies (AddBiomechanics) |
+| Trials | Subjects | Hours | Frames | Sources | Release |
+|:---:|:---:|:---:|:---:|:---:|:---:|
+| 10,386 | 140 | 27 | 9.7M | 10 studies (AddBiomechanics) | 1.3.0 (2026-10-29) |
 
 ## Demos
 
@@ -47,22 +51,51 @@ SKEL body motion paired with measured ground reaction forces (GRF), inverse-dyna
 | **Sit-to-stand** | **Drop jump** |
 | <img src="assets/sit_to_stand_uhlrich2023.gif" width="100%"> | <img src="assets/drop_jump.gif" width="100%"> |
 
-Torque |τ| (left body) and uncertainty σ<sub>τ</sub> (right body) on six activities.
+**Torque |τ| (left body) and uncertainty σ<sub>τ</sub> (right body)**
 
 <p align="center"><img src="assets/activities_grid_tau_sigma.gif" width="100%"></p>
 
-## Files
+## Quick start
 
+### 0. Download the SKEL model (required)
+
+The trial files store SKEL parameters (`poses`, `betas`, `trans`), not meshes. Register and download the SKEL model from the [SKEL website](https://skel.is.tue.mpg.de/) under its non-commercial license. ForceBody does not redistribute it. You need it to build meshes or joints. The force, torque and uncertainty arrays can be read without it.
+
+### 1. Download the data
+
+```bash
+hf download kwonjoon/ForceBody --repo-type dataset --local-dir ForceBody
 ```
-trials/<study>/S<NN>/<trial_uid>.npz   one file per trial (all arrays below)
-models/                                AddBiomechanics OpenSim models per subject (used for the torques)
-manifest.csv  (+ _train, _test)        one row per trial: metadata, split, quality flags
-persons.csv, subjects.csv              one row per subject: SKEL shape, sex, height, mass
-SCHEMA_v1.3.csv                        name, shape, unit and frame of every field
-DETAILS.md                             full documentation (masks, conventions, known issues)
+
+About 46 GB.
+
+### 2. Install the loader
+
+```bash
+git clone https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026.git
+cd ForceBody-NeurIPS-ED-2026/code
+pip install numpy
 ```
 
-Main arrays in each trial file (T = frames):
+### 3. Load a trial
+
+```python
+import forcebody_io as fio
+
+d = fio.load_trial("ForceBody/trials/Moore2015/S10/Moore2015_S10_walk_014.npz", pose_source="pass0")
+m = float(d["mass_kg_dynamics"])
+ok = d["mc_frame_ok"]                       # always apply the frame masks
+tau   = d["tau_selfconsistent"][ok] / m     # joint torque (N m / kg)
+sigma = d["sigma_tau"][ok] / m              # torque uncertainty (N m / kg)
+```
+
+For force or torque prediction, use `pose_source="pass0"` as model input.
+
+### 4. Train and evaluate
+
+See the GitHub repository (`code/baselines/`) for cache building, training, uncertainty-weighted finetuning, inference and scoring.
+
+## What is in a trial
 
 | Field | Shape | Content |
 |---|---|---|
@@ -70,23 +103,18 @@ Main arrays in each trial file (T = frames):
 | `grf_aligned`, `cop_aligned_foot` | `[T,C,3]` | Measured GRF and centre of pressure per foot |
 | `tau_selfconsistent` | `[T,37]` | Inverse-dynamics joint torque |
 | `sigma_tau`, `mu_tau` | `[T,37]` | Monte Carlo torque uncertainty and mean |
-| `*_frame_ok` | `[T]` | Frame masks. Always apply them |
-
-## Quick start
-
-**SKEL model required.** The trial files store SKEL parameters (`poses`, `betas`, `trans`). To build meshes or joints from them, download the SKEL model separately from the [SKEL website](https://skel.is.tue.mpg.de/) under its license. ForceBody does not redistribute it. The force, torque and uncertainty arrays can be used without it.
+| `*_frame_ok` | `[T]` | Frame masks |
 
-```bash
-hf download kwonjoon/ForceBody --repo-type dataset --local-dir ForceBody
-```
-
-```python
-import numpy as np
-d = np.load("ForceBody/trials/Moore2015/S10/Moore2015_S10_walk_014.npz", allow_pickle=True)
-tau = d["tau_selfconsistent"] / d["mass_kg_dynamics"]   # N m / kg
-```
+## Files
 
-For force or torque prediction, take motion input from `poses` (kinematics pass) only. See `DETAILS.md` and the GitHub loader.
+| Path | Content |
+|---|---|
+| `trials/<study>/S<NN>/<trial_uid>.npz` | One file per trial |
+| `models/` | AddBiomechanics OpenSim models per subject (used for the torques) |
+| `manifest.csv` (+ `_train`, `_test`) | One row per trial: metadata, split, quality flags |
+| `persons.csv`, `subjects.csv` | One row per subject: SKEL shape, sex, height, mass |
+| `SCHEMA_v1.3.csv` | Shape, unit and frame of every field |
+| `DETAILS.md` | Full documentation (masks, conventions, known issues) |
 
 ## License