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README.md

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 ---
 license: cc-by-4.0
+task_categories:
+  - image-segmentation
+  - object-detection
+  - video-classification
+language: []
+tags:
+  - robotics
+  - tracking
+  - articulated-objects
+  - point-tracking
+  - long-horizon
+  - sapien
+  - partnet-mobility
+  - rgb-d
+  - manipulation
+  - affordance
+  - semantic-drift
+  - embodied-ai
+  - video
+  - depth
+  - multi-view
+pretty_name: QueST PartNet-Mobility SAPIEN Dataset
+size_categories:
+  - 10K<n<100K
+dataset_info:
+  features:
+    - name: video
+      dtype: video
+    - name: affordance_visualization
+      dtype: image
+    - name: manipulation_level
+      dtype:
+        class_label:
+          names:
+            - manipulation_1
+            - manipulation_2
+            - manipulation_3
+            - manipulation_4
+    - name: take_id
+      dtype: string
+    - name: object_id
+      dtype: string
+    - name: n_joints
+      dtype: int32
+    - name: split
+      dtype: string
+    - name: has_depth
+      dtype: bool
+  splits:
+    - name: train
+      num_examples: 16000
+    - name: test
+      num_examples: 2442
 ---
+
+# QueST: PartNet-Mobility SAPIEN Simulation Dataset
+
+[![Paper](https://img.shields.io/badge/Paper-CAO%40ICLR%202026-blue)](https://arxiv.org/abs/XXXX.XXXXX)
+[![License](https://img.shields.io/badge/License-CC--BY%204.0-green)](https://creativecommons.org/licenses/by/4.0/)
+[![Downloads](https://img.shields.io/badge/Downloads-2.5K%2Fmonth-brightgreen)]()
+[![IIITA](https://img.shields.io/badge/IIIT-Allahabad-orange)](https://www.iiita.ac.in)
+
+This dataset accompanies the paper:
+
+> **QueST: Persistent Queries as Semantic Monitors for Drift Suppression in Long-Horizon Tracking**  
+> Mayank Anand, Mohammad Saqlain, Kyan Mahajan, Priya Shukla, G.C Nandi, Andrew Melnik  
+> *CAO Workshop at ICLR 2026*
+
+---
+
+## What Is This Dataset?
+
+Synchronized RGB-D simulation sequences rendered in **SAPIEN** from **PartNet-Mobility** articulated objects, designed to stress-test long-horizon point tracking under articulation, occlusion, and viewpoint change.
+
+The dataset supports the **QueST framework** — which replaces frame-to-frame Markovian tracking with persistent semantic queries that attend globally across time, achieving a **67.7% APE reduction** over TAP-Net on long-horizon articulated sequences.
+
+---
+
+## Exact Folder Structure
+
+Each sequence is stored as an individual `take` folder:
+
+```
+QueST-PartNetMobility-SAPIEN/
+│
+├── manipulation_1/              Level 1 — 1 joint actuated
+│   ├── {object_id}/
+│   │   ├── take_00/
+│   │   │   ├── frames/          RGB-D frames (PNG sequence)
+│   │   │   ├── affordance/      Pixel-level affordance maps
+│   │   │   ├── video.mp4        Full sequence video (36.6 kB avg)
+│   │   │   ├── affordance_vis_10frames.png   Visualization (455 kB)
+│   │   │   └── metadata.json    Sequence metadata (20.5 kB)
+│   │   ├── take_01/
+│   │   └── ...
+│   └── ...
+│
+├── manipulation_2/              Level 2 — 2 joints actuated
+├── manipulation_3/              Level 3 — 3 joints actuated
+└── manipulation_4/              Level 4 — 4 joints, 240 frames
+```
+
+### What Each File Contains
+
+| File | Description |
+|---|---|
+| `frames/` | Individual RGB-D frames as PNG — use for frame-level tracking evaluation |
+| `affordance/` | Pixel-level affordance annotations — interaction-relevant regions labeled |
+| `video.mp4` | Full sequence as compressed video — use for temporal model training |
+| `affordance_vis_10frames.png` | Visual summary of affordance labels across 10 evenly-spaced frames |
+| `metadata.json` | Object ID, joint configuration, ground truth 3D trajectories, camera intrinsics |
+
+---
+
+## Complexity Levels
+
+| Level | Folder | Joints actuated | Max frames | Purpose |
+|---|---|---|---|---|
+| 1 | `manipulation_1` | 1 | ~60 | Short-horizon training baseline |
+| 2 | `manipulation_2` | 2 | ~120 | Medium complexity |
+| 3 | `manipulation_3` | 3 | ~180 | Hard multi-joint sequences |
+| 4 | `manipulation_4` | **4** | **240** | Long-horizon stress test |
+
+Each level actuates joints **sequentially** — Level 4 is the cumulative long-horizon challenge designed to expose drift in Markovian trackers.
+
+---
+
+## Key Statistics
+
+| Property | Value |
+|---|---|
+| Total images / rows | 18,442 |
+| Total size | 2.27 GB |
+| Camera viewpoints | 3 synchronized RGB-D views per sequence |
+| Renderer | SAPIEN (physics-based) |
+| Depth data | Included in frames/ |
+| Annotations | Pixel-level affordance + 3D ground-truth trajectories |
+| Articulation types | Revolute, prismatic |
+| Object categories | Storage furniture, appliances, hinged devices |
+
+---
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+ds = load_dataset("AnandMayank/QueST-PartNetMobility-SAPIEN")
+
+# Load only long-horizon sequences (manipulation_4)
+ds = load_dataset(
+    "AnandMayank/QueST-PartNetMobility-SAPIEN",
+    data_files={"train": "manipulation_4/**/*"}
+)
+```
+
+### Loading metadata for a specific take
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+# Download metadata for a specific take
+meta_path = hf_hub_download(
+    repo_id="AnandMayank/QueST-PartNetMobility-SAPIEN",
+    filename="manipulation_1/35059/take_00/metadata.json",
+    repo_type="dataset"
+)
+
+with open(meta_path) as f:
+    meta = json.load(f)
+
+print(meta.keys())
+# dict_keys(['object_id', 'joint_config', 'trajectory_gt',
+#            'camera_intrinsics', 'affordance_labels', ...])
+```
+
+### Loading frames for tracking evaluation
+
+```python
+from huggingface_hub import snapshot_download
+import os
+from PIL import Image
+
+# Download a single take
+path = snapshot_download(
+    repo_id="AnandMayank/QueST-PartNetMobility-SAPIEN",
+    repo_type="dataset",
+    allow_patterns="manipulation_4/*/take_00/**"
+)
+
+# Load frames in order
+frames_dir = os.path.join(path, "manipulation_4/35059/take_00/frames")
+frames = sorted([
+    Image.open(os.path.join(frames_dir, f))
+    for f in os.listdir(frames_dir)
+    if f.endswith(".png")
+])
+print(f"Loaded {len(frames)} frames")
+```
+
+---
+
+## Benchmark Results
+
+| Method | APE ↓ | Drift@100 ↓ | Identity Acc ↑ |
+|---|---|---|---|
+| RAFT-3D | 0.341 | 0.472 | 8.7% |
+| CoTracker | 0.276 | 0.398 | 19.2% |
+| TAP-Net | 0.251 | 0.372 | 21.4% |
+| **QueST (ours)** | **0.081** | **0.155** | **86.5%** |
+
+QueST achieves **67.7% APE reduction** over TAP-Net — the strongest prior method — while maintaining bounded error growth vs near-linear drift in baselines.
+
+---
+
+## Reproducing Results
+
+```bash
+git clone https://github.com/AnandMayank/QueST
+cd QueST
+pip install -r requirements.txt
+
+# Download dataset
+python scripts/download_dataset.py \
+  --repo AnandMayank/QueST-PartNetMobility-SAPIEN \
+  --output data/
+
+# Evaluate on Level 4 long-horizon sequences
+python evaluate.py \
+  --data data/manipulation_4 \
+  --checkpoint checkpoints/quest_full.ckpt \
+  --level 4
+```
+
+---
+
+## Citation
+
+If you use this dataset please cite:
+
+```bibtex
+@inproceedings{anand2026quest,
+  title     = {QueST: Persistent Queries as Semantic Monitors for 
+               Drift Suppression in Long-Horizon Tracking},
+  author    = {Anand, Mayank and Saqlain, Mohammad and Mahajan, Kyan 
+               and Shukla, Priya and Nandi, G.C. and Melnik, Andrew},
+  booktitle = {CAO Workshop at ICLR 2026},
+  year      = {2026}
+}
+```
+
+---
+
+## License
+
+[Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
+
+Free to use for any purpose including commercial use, with attribution.
+
+---
+
+## Contact
+
+**IIIT Allahabad** — Department of Information Technology  
+Mayank Anand · [iit2024036@iiita.ac.in](mailto:iit2024036@iiita.ac.in)  
+G.C. Nandi · [gcnandi@iiita.ac.in](mailto:gcnandi@iiita.ac.in)
+
+**University of Bremen**  
+Andrew Melnik · [andrew.melnik.papers@gmail.com](mailto:andrew.melnik.papers@gmail.com)
+
+Issues and questions: [github.com/AnandMayank/QueST](https://github.com/AnandMayank/QueST)