Datasets:

Imagirep
/

ImagiRep

pretty_name: ImagiRep
license: other
license_name: imagirep-license
license_link: LICENSE.md
language:
  - en
task_categories:
  - visual-question-answering
  - multiple-choice
tags:
  - spatial-reasoning
  - world-models
  - visual-imagination
  - matterport3d
  - benchmark
size_categories:
  - n<1K
extra_gated_heading: Access to ImagiRep requires agreeing to the Matterport3D Terms of Use
extra_gated_prompt: >-
  ImagiRep contains images, videos and geometry rendered from the Matterport3D
  dataset. Before requesting access, you must have read and agreed to the
  Matterport3D Terms of Use (https://kaldir.vc.in.tum.de/matterport/MP_TOS.pdf).
  The data may be used for non-commercial academic research only and may not be
  redistributed.
extra_gated_fields:
  Name: text
  Affiliation: text
  I have agreed to the Matterport3D Terms of Use: checkbox
  I will use ImagiRep for non-commercial research only and will not redistribute it: checkbox
configs:
  - config_name: questions
    data_files:
      - split: test
        path: benchmark/benchmark922/questions.jsonl

ImagiRep

Benchmark data for the paper Representing the Unseen: Rethinking Visual Imagination for Spatial Reasoning (Zengqi Zhao*, Zun Wang*, Shoubin Yu, Mohit Bansal, Yue Zhang; UNC Chapel Hill; * equal contribution).

Project page · Paper (arXiv) · Code and results (GitHub)

ImagiRep has 922 four-option spatial reasoning questions about unobserved viewpoints in Matterport3D scenes:

setting	questions	groups	scenes	question types
Viewpoint Imagination	622	200 trajectories	73	Open-space Direction (91), Nearest-obstacle Direction (55), Visibility (104), Nearest Object (104), Farthest Object (98), Object-relative Direction (170)
Cross-view Integration	300	143 view groups	21	Nearest Object (81), Farthest Object (82), Spatial Ordering (137)

For every question the imagined (unobserved) state is given as endpoint RGB and six alternative representations of the same state: top-down geometry, spatial text, video, depth, surface normals and panorama. The dataset also holds the estimated representations used in the paper (from HY-World 2.0 for Viewpoint Imagination, and from Stable Virtual Camera with MoGe-2 depth for Cross-view Integration).

path	content
`benchmark/benchmark922/questions.jsonl`	922 rows: question, options, gold answer, system prompt, and the evidence of every input condition
`benchmark/benchmark922/tasks.jsonl`	7,376 model-ready requests (922 questions x 8 conditions) as ordered parts `{"text"}`, `{"image"}`, `{"video"}`
`benchmark/benchmark922/images/`, `videos/`	all images and videos referenced by the two files above; `SHA256SUMS`
`benchmark/uid_lists_and_index/`	uid lists and `question_index.tsv` (category, gold, trajectory or view group, scan, look-back flag)
`benchmark/camera_poses/`	camera poses and Matterport3D scan / viewpoint ids for every question
`benchmark/randomization/`	how each question was randomized from its annotation (option order; image numbering for Cross-view Integration)
`03_estimated_viewpoint_gpt61sol/`	estimated representations for Viewpoint Imagination: `estimated_assets/` and the exact request images `gpt_runs/images/`
`04_estimated_crossview_gpt61sol/`	estimated RGB and depth for Cross-view Integration: `gpt_runs/inputs/` and `gpt_runs/images/`

The folders follow the layout of the GitHub repository: clone it and download this dataset into its root, and the scripts there find every file (hf download Imagirep/ImagiRep --repo-type dataset --local-dir .). The request files and per-question model outputs that use the estimated images are in the GitHub repository.

Input conditions: observed (observed view(s)), oracle (+ endpoint RGB; for look-back questions a view from the endpoint facing the start) and oracle_<m> (+ one representation m: topdown, text, video, depth, surface_normals, panorama).

import json
qs = [json.loads(l) for l in open("benchmark/benchmark922/questions.jsonl")]
q = qs[0]
print(q["uid"], q["category"], q["gold"])
print(q["question_prompt"])
print(q["conditions"]["oracle_panorama"])   # text parts and file names in images/ and videos/

License and terms

See LICENSE.md. Images, videos and geometry are derived from Matterport3D and remain subject to the Matterport3D Terms of Use: non-commercial academic research only, no redistribution. Questions, labels and other annotations are released under CC BY-NC 4.0.

Citation

@article{zhao2026imagirep,
  title   = {Representing the Unseen: Rethinking Visual Imagination for Spatial Reasoning},
  author  = {Zhao, Zengqi and Wang, Zun and Yu, Shoubin and Bansal, Mohit and Zhang, Yue},
  journal = {arXiv preprint arXiv:2610.37437},
  year    = {2026}
}

ImagiRep

Contents

License and terms

Citation