Slim down Model Card

README.md

@@ -15,64 +15,23 @@ library_name: pytorch
 
 # EmLoco — CVPR 2025 release assets
 
-This repository hosts the **`Ours` (num_modes=1) checkpoints** and **CVPR-era preprocessed shards** that accompany:
+Checkpoints and preprocessed shards for
+**Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment** (CVPR 2025).
 
-> **Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment** (CVPR 2025).
-> Hiromu Taketsugu, Takeru Oba, Takahiro Maeda, Shohei Nobuhara, Norimichi Ukita.
-> [📄 paper](https://openaccess.thecvf.com/content/CVPR2025/html/Taketsugu_Physical_Plausibility-aware_Trajectory_Prediction_via_Locomotion_Embodiment_CVPR_2025_paper.html) · [arXiv 2503.17267](https://arxiv.org/abs/2503.17267) · [project page](https://iminthemiddle.github.io/EmLoco-Page/) · [🐙 source code](https://github.com/ImIntheMiddle/EmLoco)
+📄 [paper](https://openaccess.thecvf.com/content/CVPR2025/html/Taketsugu_Physical_Plausibility-aware_Trajectory_Prediction_via_Locomotion_Embodiment_CVPR_2025_paper.html) · 🐙 [code](https://github.com/ImIntheMiddle/EmLoco)
 
-## What's here
+## Contents
 
-| Path | Size | Content |
-|---|---|---|
-| `checkpoints/jta_ours/` | 38 MB | JTA Ours model (num_modes=1, EmLoco loss, token_num=49) — `best_val_checkpoint` of `jta_valuenet_100` |
-| `checkpoints/jrdb_ours/` | 38 MB | JRDB Ours model (num_modes=1, EmLoco loss, token_num=26) — last `checkpoint` of `jrdb_value150` |
-| `preprocess_smpl_cvpr/jta_all_visual_cues/{train,val,test}/` | 23 GB | JTA J=49 shards (torch 2.x zip-format `.pt`, 21 parts) |
-| `preprocess_smpl_cvpr/jrdb_all_visual_cues/{train,val,test}/` | 4.5 GB | JRDB J=26 shards (`.pkl`, 7 parts). Pose tokens are NaN-filled for frames without a JRDB-Act label so the EmLoco loss skips them automatically. |
+- `checkpoints/{jta_ours, jrdb_ours}/` — Ours model (num_modes=1)
+- `preprocess_smpl_cvpr/{jta, jrdb}_all_visual_cues/{train, val, test}/` — CVPR-era preprocessed shards
 
-Each checkpoint directory also contains a `config.yaml` capturing the training-time hyper-parameters.
+## Usage
 
-## Quick start
-
-```bash
-# 0. Clone EmLoco source + set up the unified Python 3.8 / CUDA 12.1 env
-git clone https://github.com/ImIntheMiddle/EmLoco && cd EmLoco
-uv sync && source .venv-22.04/bin/activate
-
-# 1. Pull the assets from this repo
-pip install -U "huggingface_hub[cli]"
-hf download iminthemiddle/EmLoco --local-dir .assets --repo-type model
-
-# 2. Wire them into the expected paths
-ln -s "$PWD/.assets/preprocess_smpl_cvpr/jta_all_visual_cues" \
-      social-transmotion/data/jta_all_visual_cues/preprocess_smpl_cvpr
-ln -s "$PWD/.assets/preprocess_smpl_cvpr/jrdb_all_visual_cues" \
-      social-transmotion/data/jrdb_all_visual_cues/preprocess_smpl_cvpr
-mkdir -p social-transmotion/experiments/{JTA,JRDB}
-ln -s "$PWD/.assets/checkpoints/jta_ours"  social-transmotion/experiments/JTA/jta_ours
-ln -s "$PWD/.assets/checkpoints/jrdb_ours" social-transmotion/experiments/JRDB/jrdb_ours
-
-# 3. Grab the LocoVal value-network from the GitHub Release (28 KB)
-mkdir -p pacer/output/exp/pacer
-# https://github.com/ImIntheMiddle/EmLoco/releases/tag/checkpoints  (unzip valuenet_checkpoints.zip)
-
-# 4. Evaluate the released Ours checkpoints
-python social-transmotion/evaluate_jta.py  --exp_name jta_ours  --modality traj+all   # ADE 0.951 / FDE 1.921
-python social-transmotion/evaluate_jrdb.py --exp_name jrdb_ours --modality traj+all   # ADE 0.369 / FDE 0.724
-```
-
-## Reproduced numbers (JRDB-Traj test, JTA-Dataset test)
-
-| Setting | ADE | FDE |
-|---|---|---|
-| JTA Ours (`jta_ours`, num_modes=1) | **0.951** | **1.921** |
-| JRDB Ours (`jrdb_ours`, num_modes=1) | **0.369** | **0.724** |
-
-For the multi-modal Table rows (num_modes>1, LocoVal filter at inference), train your own with `--multi_modal --valueloss_w 1.0` on top of `preprocess_smpl_cvpr/` or contact the authors for additional checkpoints.
+See the [GitHub README](https://github.com/ImIntheMiddle/EmLoco#-data--checkpoints-hugging-face) for download + setup instructions.
 
 ## License
 
-The assets in this repository are released under **CC BY-NC 4.0** — *research, non-commercial use only*. The reason is that the underlying SMPL body model and the JTA / JRDB datasets used during preprocessing and training are themselves restricted to non-commercial research use, as is the PACER (NVIDIA) code that produced the LocoVal value function. The EmLoco source code on GitHub remains under MIT.
+CC BY-NC 4.0 (research, non-commercial). The underlying SMPL / JTA / JRDB / PACER assets restrict commercial use.
 
 ## Citation