Datasets:
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Tags:
multimodal
image-to-video
video-generation
object-centric-evaluation
world-models
memory-evaluation
| pretty_name: OPIS Dataset | |
| tags: | |
| - multimodal | |
| - image-to-video | |
| - video-generation | |
| - object-centric-evaluation | |
| - world-models | |
| - memory-evaluation | |
| size_categories: | |
| - 1K<n<10K | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: metadata.jsonl | |
| # OPIS Dataset | |
| **OPIS** is an input-grounded benchmark for evaluating multi-object memory in | |
| image-to-video and camera-conditioned video world models. It anchors every | |
| evaluation to the fixed object instances visible in the initial image, rather | |
| than to a generated history or a prescribed reference video. | |
| The benchmark is designed to measure whether a generated rollout preserves the | |
| objects established by its input. Its evaluator reports three complementary | |
| dimensions: **Presence** (whether observable instances are accounted for), | |
| **Identity** (whether the particular input instances are preserved), and | |
| **Structure** (whether their geometry or structural properties persist). | |
| [](https://arxiv.org/abs/2609.35052) | |
| [](https://huggingface.co/datasets/Kirito-Lab/OPIS-datase) | |
| [](https://github.com/SSStarain/OPIS) | |
|  | |
| ## Dataset summary | |
| | Item | Value | | |
| | --- | ---: | | |
| | Cases | 500 | | |
| | Scene domains | 3 | | |
| | Subcategories | 10 | | |
| | Addressable object instances | 12,672 | | |
| | Static-geometry instances | 6,128 | | |
| | Dynamic-structure instances | 6,544 | | |
| | Reference image resolution | 1280 × 720 RGB | | |
| | Case index range per subcategory | 0001–0050 | | |
| The three scene domains are: | |
| | Directory | Domain | Subcategories | Cases | | |
| | --- | --- | --- | ---: | | |
| | `01_real_world_scene` | Real-world scenes | `home_indoor`, `natural_outdoor`, `public_indoor`, `urban_outdoor` | 200 | | |
| | `02_robot_embodied` | Embodied and robotic environments | `industrial_robot_env`, `robot_lab_setup`, `simulated_environment` | 150 | | |
| | `03_game_virtual_world` | Game and virtual worlds | `Cartoon_style_3D`, `Pixel_style_3D_sandbox`, `Realistic_style_3D_world` | 150 | | |
| Each of the ten subcategories contains 50 cases. Case IDs and image names use | |
| the stable convention `<subcategory>_<four_digit_index>`, for example | |
| `home_indoor_0001` or `Cartoon_style_3D_0042`. | |
| ## Sources and construction | |
| The benchmark combines real-world imagery, game screenshots, simulator-rendered | |
| scenes, and generated images. The source families described in the associated | |
| paper include Visual Genome, ADE20K, COCO images indexed through RefCOCO, | |
| BridgeData V2, IndustryShapes, gameplay collections such as Minecraft and | |
| SuperTuxKart, locally rendered AI2-THOR scenes, and images generated with | |
| `gpt-image-2.5-sunburst`. | |
| All cases are converted to a common input-grounded format. A VLM-assisted | |
| selection and noun-phrase cleaning stage is followed by instance segmentation, | |
| appearance embedding extraction, mobility/kinematic annotation, geometric | |
| reference construction, task-prompt creation, and case-level human review. | |
| The released directory contains the resulting benchmark artifacts; it is not a | |
| collection of generated continuation videos. | |
| ## Repository layout | |
| ```text | |
| OPIS-dataset/ | |
| ├── metadata.jsonl # one JSON record per case (500 records) | |
| ├── metadata_summary.json # dataset-wide counts and naming convention | |
| ├── assets/ | |
| │ ├── fig2.pdf # original paper figure | |
| │ └── fig2.png # page preview used above | |
| ├── <domain>/ | |
| │ └── <subcategory>/<case_id>/ | |
| │ ├── <case_id>.jpg | |
| │ ├── noun_phrases.txt | |
| │ ├── text_prompt.txt | |
| │ ├── reference_scene.json | |
| │ ├── reference_geometry.npz | |
| │ └── assets/ | |
| │ ├── preprocessed/raw_1280x720.png | |
| │ ├── masks/ref_*.png | |
| │ └── crops/ref_*.png | |
| ``` | |
| Here `<domain>` is one of `01_real_world_scene`, `02_robot_embodied`, or | |
| `03_game_virtual_world`. The three domain directories each contain the same | |
| two-level `<subcategory>/<case_id>` case layout. | |
| ## Manifest fields | |
| `metadata.jsonl` is a line-delimited JSON manifest. Each record contains: | |
| - `case_id`: stable case identifier; | |
| - `category`: one of `real_world_scene`, `robot_embodied`, or | |
| `game_virtual_world`; | |
| - `subcategory`: one of the ten subcategories listed above; | |
| - `final_case_path`: path to the complete case directory relative to the dataset | |
| root; | |
| - `image_path`: path to the 1280 × 720 initial image; | |
| - `noun_phrases_path`: path to the case-level noun-phrase list; | |
| - `annotation.noun_phrases`: cleaned object concepts used to construct the | |
| reference inventory; | |
| - `annotation.object_count`: number of annotated object instances; | |
| - `annotation.status`: annotation status (`annotated` in this release); | |
| - `image`: image format, color mode, and dimensions; | |
| - `schema_version`: public manifest schema version. | |
| Example: | |
| ```json | |
| { | |
| "case_id": "home_indoor_0001", | |
| "category": "real_world_scene", | |
| "subcategory": "home_indoor", | |
| "final_case_path": "01_real_world_scene/home_indoor/home_indoor_0001", | |
| "image_path": "01_real_world_scene/home_indoor/home_indoor_0001/home_indoor_0001.jpg", | |
| "noun_phrases_path": "01_real_world_scene/home_indoor/home_indoor_0001/noun_phrases.txt", | |
| "annotation": { | |
| "noun_phrases": ["wooden table", "chair"], | |
| "noun_phrase_count": 2, | |
| "object_count": 14, | |
| "status": "annotated" | |
| }, | |
| "image": {"format": "JPEG", "mode": "RGB", "size": [1280, 720]}, | |
| "schema_version": "final-public-v1" | |
| } | |
| ``` | |
| ## Case-level artifacts | |
| ### Initial image and task instruction | |
| - `<case_id>.jpg` is the initial observation supplied to a video-generation | |
| model. | |
| - `text_prompt.txt` contains the case-specific camera-motion and memory | |
| challenge instruction. It describes a continuous viewpoint change, allows | |
| physically plausible motion, and asks the rollout to preserve the input | |
| instances without adding, removing, cloning, or merging objects. | |
| - `noun_phrases.txt` contains the cleaned object concepts used for reference | |
| construction. | |
| ### Object reference | |
| `reference_scene.json` contains the fixed input-grounded object inventory. For | |
| each instance it stores an object ID, category, bounding box, center, size and | |
| appearance attributes, an appearance embedding, the paths to its mask and crop, | |
| and mobility metadata. The mobility metadata assigns each instance to either | |
| the `static_geometry` or `dynamic_structure` evaluation track. | |
| The associated files are: | |
| - `assets/masks/ref_*.png`: instance masks aligned to the initial image; | |
| - `assets/crops/ref_*.png`: object crops used for appearance evidence; | |
| - `assets/preprocessed/raw_1280x720.png`: normalized processing image. | |
| ### Geometry cache | |
| `reference_geometry.npz` is a NumPy archive containing the precomputed | |
| input-grounded geometric evidence used by the evaluator. Depending on the case, | |
| it contains `points`, `depth`, `intrinsics`, `valid`, `background_mask`, one or | |
| more `query_*` arrays, and `manifest`. | |
| The `manifest` array is a UTF-8 JSON document stored inside the archive. It | |
| records array integrity hashes, query IDs, schema information, and the | |
| configuration needed to interpret the geometry cache. It does not contain | |
| generated videos or evaluation results. | |
| ## Loading the manifest | |
| ```python | |
| import json | |
| from pathlib import Path | |
| root = Path("OPIS-dataset") | |
| rows = [json.loads(line) for line in (root / "metadata.jsonl").read_text().splitlines()] | |
| row = rows[0] | |
| image = root / row["image_path"] | |
| case_dir = root / row["final_case_path"] | |
| print(row["case_id"], image, case_dir) | |
| ``` | |
| To inspect a reference geometry archive: | |
| ```python | |
| import json | |
| import numpy as np | |
| with np.load(case_dir / "reference_geometry.npz", allow_pickle=True) as archive: | |
| manifest = json.loads(archive["manifest"].tobytes().decode("utf-8")) | |
| print(archive.files) | |
| print(manifest["schema_version"]) | |
| ``` | |
| ## Intended evaluation protocol | |
| For each case, a model receives the initial image and `text_prompt.txt` and | |
| produces a video rollout. The evaluator compares sampled generated frames with | |
| the fixed object reference derived from the initial image. Camera motion and | |
| partial occlusion are allowed; an object that is not observable is treated | |
| separately from a confirmed memory failure. The benchmark therefore evaluates | |
| observable input-instance preservation, not pixel-level imitation of a single | |
| target continuation. | |
| The released dataset is sufficient to prepare inputs and run the reference-side | |
| evaluation pipeline. Model checkpoints, inference services, generated videos, | |
| and model-specific evaluation outputs are not included in this repository. | |
| ## Citation | |
| If OPIS is useful in your research, please cite the paper: | |
| ```bibtex | |
| @misc{wang2026opisinputgroundedbenchmarkmultiobject, | |
| title={OPIS: An Input-Grounded Benchmark for Multi-Object Memory in Video World Models}, | |
| author={Hao Wang and Tao Yu and Liuzhou Zhang and HeXin Wang and Haopeng Jin and Yuxuan Zhou and Xinming Wang and Hongzhu Yi and Xinye Li and Yuanlei Wang and Ping Nie and Yan Huang and Yuxuan Zhang and Pengfei Zhou and Yanyan Zou and Wei Yang}, | |
| year={2026}, | |
| eprint={2609.35052}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2609.35052}, | |
| } | |
| ``` | |