Upload README.md

README.md

@@ -118,10 +118,9 @@ The dataset is designed to support research on physics-aware video generation, f
 | Leaderboard           | `░░░░░░░░░░` 0%               | Ongoing      | Link will be added when available |
 | Baseline code         | `░░░░░░░░░░` 0%               | Not released | Expected around June              |
 | Data processing       | `░░░░░░░░░░` 0%               | Not released | Expected around June              |
+| SubSet                | `██████████`100%              | Released     |                                   |
 
-## 🔎 Dataset Viewer
-
-The Hugging Face Dataset Viewer is designed to help users quickly search, filter, and export scene-level metadata without downloading the full dataset.
+## 📥 How to Use
 
 ### Filtering and Search
 
@@ -133,128 +132,49 @@ The viewer should support filtering by:
 - **Split:** `train`, `val`, `test`
 - **Availability:** rendered data, 3D assets, annotations, benchmark subset
 
-### Viewer Table
-
-After selecting filters, the viewer should display a dataframe-like table with at least the following columns:
-
-| Column            | Description                                                   |
-| ----------------- | ------------------------------------------------------------- |
-| `id`              | Unique scene identifier                                       |
-| `scene_name`      | Human-readable scene name                                     |
-| `split`           | Train / validation / test split                               |
-| `activity_type`   | Single-, double-, or triple-physics activity                  |
-| `physical_domain` | Mechanics, fluid dynamics, optics, or magnetism               |
-| `phenomena`       | Physical phenomena involved in the scene                      |
-| `ue_path`         | Unreal Engine scene or asset path                             |
-| `repo_link`       | Link to the corresponding repository item or hosted data page |
-| `download_link`   | Direct download link for the scene package or rendered data   |
-
-### JSON Export
-
-The viewer should provide an **Export JSON** button. The exported JSON should contain selected scenes and their download links.
-
-Example export format:
-
-```json
-{
-  "selected_scenes": [
-    {
-      "id": "scene_000000",
-      "scene_name": "TODO",
-      "download_link": "TODO"
-    },
-    {
-      "id": "scene_000001",
-      "scene_name": "TODO",
-      "download_link": "TODO"
-    }
-  ]
-}
-```
-
-This JSON file can be passed directly to the download script.
-
-## 📥 How to Use
-
-Install Dependencies
+### Install Dependencies
 
 ```bash
 pip install datasets huggingface_hub pandas tqdm
 ```
 
-### Download Metadata Only
-
-```bash
-huggingface-cli download TODO/PhysInOne \
-  --include "metadata/*" \
-  --local-dir ./PhysInOne
-```
-
-### Download by Exported JSON
-
-After selecting scenes in the Dataset Viewer, export the selected scene list as JSON and download the corresponding files:
+### Download a Split
 
 ```bash
 python scripts/download.py \
-  --selection selected_scenes.json \
+  --split train \
   --output_dir ./PhysInOne
 ```
 
-### Download a Split
+### Download by Exported JSON
 
 ```bash
 python scripts/download.py \
   --split train \
+  --activity_type double \
+  --domain mechanics \
+  --phenomena P01 P03 \
+  --modalities rgb depth seg \
   --output_dir ./PhysInOne
 ```
 
+### Download by Exported JSON
+
 ```bash
-python scripts/download.py \
-  --split val \
-  --output_dir ./PhysInOne
+python scripts/filter_cases.py \
+  --split train \
+  --domain mechanics \
+  --activity_type double \
+  --phenomena P01 P03 \
+  --output selected_cases.json
 ```
 
 ```bash
 python scripts/download.py \
-  --split test \
+  --selection selected_scenes.json \
   --output_dir ./PhysInOne
 ```
 
-### Load Metadata
-
-```python
-import json
-from pathlib import Path
-
-metadata_path = Path("./PhysInOne/metadata/train.jsonl")
-
-with open(metadata_path, "r") as f:
-    sample = json.loads(next(f))
-
-print("ID:", sample["id"])
-print("Scene name:", sample["scene_name"])
-print("Split:", sample["split"])
-print("Physical domains:", sample["physical_domains"])
-print("Phenomena:", sample["physical_phenomena"])
-print("Download link:", sample["download_link"])
-```
-
-### Visualize a Scene
-
-```bash
-python scripts/visualize_sample.py \
-  --scene_id scene_000000 \
-  --data_root ./PhysInOne
-```
-
-## 🎬 Benchmark Subsets
-
-We provide mini benchmark subsets for lightweight evaluation and quick prototyping.
-
-| Subset      | Size       | Intended Use                                     |
-| ----------- | ----------:| ------------------------------------------------ |
-| `test-mini` | 103 scenes | Long-term and short-term future frame prediction |
-
 ## 🎬 Visual Overview
 
 <p align="center">
@@ -303,7 +223,7 @@ PhysInOne/
     └── visualize_sample.py
 ```
 
-### Data Splits
+### 📊 Data Splits
 
 PhysInOne is split into train, validation, and test sets. Each split is intended for a different stage of model development and evaluation.
 
@@ -516,87 +436,17 @@ annotations/pointclouds/{split}/{scene_id}/points.ply
 
 Please specify whether point colors, normals, semantic labels, or instance labels are included in the `.ply` file.
 
-## Supported Tasks and Benchmarks
+## 🧪 Supported Tasks and Benchmarks
 
 PhysInOne supports the following visual physics learning and reasoning tasks.
 
-### Physics-aware Video Generation
-
-Given text prompts, image conditions, or initial frames, models generate videos that should be visually realistic and physically plausible.
-
-Representative settings:
-
-- Text-to-video generation
-- Image-to-video generation
-- Text-image-to-video generation
-- Video model fine-tuning with physics-rich data
-
-Suggested metrics:
-
-- PMF: Physical Motion Fidelity
-- FVD
-- Human physical plausibility rating
-
-### Long-term Future Frame Prediction
-
-Given the first half of a dynamic scene, models predict the second half of the video.
-
-Representative settings:
-
-- Seen-view prediction
-- Novel-view prediction
-- Scene-specific 4D modeling
-- Video prediction
-
-Suggested metrics:
-
-- PMF
-- PSNR
-- SSIM
-- LPIPS
-
-### Continuous Short-term Future Frame Prediction
-
-Given streaming observations, models continuously predict the next few frames.
-
-This setting is useful for:
-
-- Future-aware robot planning
-- Embodied AI
-- Short-horizon physical prediction
-- Dynamic scene understanding
-
-Suggested metrics:
-
-- PMF
-- PSNR
-- SSIM
-- LPIPS
-
-### Physical Property Estimation
-
-Given visual observations, models estimate physical properties of scene objects and materials.
-
-Example target properties:
-
-- Young's modulus
-- Poisson's ratio
-- Viscosity
-- Bulk modulus
-- Yield stress
-- Friction angle
-- Initial velocity
-
-### Motion Transfer
+- Physics-aware Video Generation
 
-Given a source video and a target image or target scene, models transfer physically meaningful motion patterns while preserving the target appearance.
+- Long-term and Short-term Future Frame Prediction
 
-Suggested metrics:
+- Physical Property Estimation
 
-- PMF
-- PSNR
-- SSIM
-- LPIPS
+- Motion Transfer
 
 ## 📜 License