Datasets:
id stringlengths 19 21 | question stringlengths 136 638 | answer stringclasses 5
values | image dict | not_sure stringclasses 3
values |
|---|---|---|---|---|
Collision_Level_1_0 | "You can see a toy retro vehicle with a classic design on the floor. Is there a chance that it will (...TRUNCATED) | B | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | C |
Collision_Level_1_1 | "You can see a toy retro vehicle with a classic design on the floor. What action should be taken to (...TRUNCATED) | D | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | E |
Collision_Level_1_2 | "There is a toy pickup truck with a retro look on the floor. Is there a chance that it will hit some(...TRUNCATED) | A | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | C |
Collision_Level_1_3 | "There is a toy pickup truck with a retro look on the floor. To avoid a collision, what should be do(...TRUNCATED) | A | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | E |
Collision_Level_1_4 | "On the floor, there is a toy retro vehicle with a retro look. Is there any risk if it moves forward(...TRUNCATED) | B | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | C |
Collision_Level_1_5 | "On the floor, there is a toy retro vehicle with a retro look. To prevent a collision, what should b(...TRUNCATED) | D | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | E |
Collision_Level_1_6 | "A toy wooden body pickup truck with a classic design is placed on the floor. Is it risky for it to (...TRUNCATED) | B | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | C |
Collision_Level_1_7 | "A toy wooden body pickup truck with a classic design is placed on the floor. What measures should b(...TRUNCATED) | D | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | E |
Collision_Level_1_8 | "There is a toy wooden truck with a stylish look on the floor. Is there any risk if it moves forward(...TRUNCATED) | B | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | C |
Collision_Level_1_9 | "There is a toy wooden truck with a stylish look on the floor. What action should be taken to preven(...TRUNCATED) | D | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABQAAAALQCAYAAADPfd1WAAAANmVYSWZNTQAqAAAAGAAAAEgAAAABAAAASAAAAAEAAg(...TRUNCATED) | E |
CausalSpatial
CausalSpatial is a visual question answering benchmark for evaluating object-centric causal spatial reasoning in vision-language models. Each question presents a 3D-rendered scene and asks the model to reason about physical outcomes — not just what is visible, but what would happen given a specific action or trajectory.
Dataset Structure
Synthetic subsets (collision, compatibility, occlusion, physics)
| Field | Type | Description |
|---|---|---|
id |
string | Unique sample identifier |
question |
string | Multiple-choice question |
answer |
string | Correct option label (e.g. A, B) |
image |
image | Rendered scene image |
not_sure |
string | Option label corresponding to "Not sure" |
Real-world subset (realworld)
| Field | Type | Description |
|---|---|---|
id |
string | Unique sample identifier |
type |
string | Question category (collision / occlusion / compatibility / trajectory) |
question |
string | Multiple-choice question |
options |
list[string] | Answer choices (e.g. ["Yes", "No"]) |
answer |
string | Correct answer text |
image |
image | Real-world scene photo |
Subsets
collision — 826 samples
A toy car is placed on a floor with objects nearby. The model must judge whether the car will collide with something if it moves forward, and if so, which object to remove to prevent the collision.
Note: Floor strip spacing encodes depth perspective.
compatibility — 99 samples
An object is suspended above a container. The model must judge whether the object can fit into the container if it falls freely.
Note: The falling object is positioned directly above the container.
occlusion — 189 samples
A car is parked in front of a cabinet. The model must judge whether an item inside the cabinet can be retrieved without being blocked, depending on the car's direction of movement.
physics — 311 samples
Ball trajectory prediction across three sports scenarios:
- Soccer: Will the ball enter the goal along the shown trajectory?
- Basketball: Will the ball go through the basket along the shown arc?
- Billiard: Will a ball reach a pocket given the cue ball's direction?
realworld — 116 samples
Real-world driving and street scene photographs. The model must reason about physical outcomes across four question types:
- collision: Will a moving vehicle collide with another object?
- occlusion: Will an object be occluded given a movement?
- compatibility: Can an object fit into or pass through a space?
- trajectory: What trajectory will a moving object follow?
Usage
from datasets import load_dataset
ds = load_dataset("Mwxinnn/CausalSpatial", name="collision")
ds = load_dataset("Mwxinnn/CausalSpatial", name="physics")
ds = load_dataset("Mwxinnn/CausalSpatial", name="compatibility")
ds = load_dataset("Mwxinnn/CausalSpatial", name="occlusion")
ds = load_dataset("Mwxinnn/CausalSpatial", name="realworld")
print(ds["train"][0])
License
MIT
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