--- 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 **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)