Datasets:

bodhisattamaiti
/

Prompt2SceneBench

objects list	surfaces list	scenes list	object_to_scenes dict	object_to_surfaces dict	surface_to_scenes dict	prompt_templates dict
[ "chair", "vase", "pillow", "blanket", "cushion", "stack of books", "table lamp", "bowl", "coffee mug", "flower pot", "stool", "tote bag", "plate", "basket of fruits", "vacuum cleaner", "vintage radio", "wicker basket", "jar", "toaster", "pair of slippers", "guitar", "paperweight", "notebook", "rug", "stack of towels", "alarm clock", "vinyl record player", "tray", "plant in pot", "air purifier", "suitcase", "doormat", "blender", "bean bag", "shoe rack", "knitted pouf", "storage bin", "mirror frame", "helmet", "pair of shoes", "basketball", "football", "tennis racquet", "sofa", "fire extinguisher", "water bottle", "laundry basket", "pair of gloves", "baseball bat", "baseball" ]	[ "wooden table", "glass shelf", "kitchen island", "bookshelf", "window sill", "ottoman", "side table", "coffee table", "nightstand", "fireplace mantel", "dresser top", "dining table", "desk surface", "wardrobe top", "bench", "bathroom counter", "staircase step", "bedside cabinet", "entryway table", "floor" ]	[ "living room", "bedroom", "kitchen", "dining room", "study room", "home office", "bathroom", "laundry room", "entryway", "reading nook", "walk-in closet", "attic", "basement", "pantry", "mudroom" ]	{ "chair": [ "dining room", "study room", "bedroom", "living room" ], "vase": [ "living room", "dining room", "entryway" ], "pillow": [ "bedroom", "living room" ], "blanket": [ "bedroom", "living room" ], "cushion": [ "bedroom", "living room", "reading nook" ], "stack of books": [ "study room", "home office", "reading nook", "bedroom" ], "table lamp": [ "bedroom", "study room", "home office" ], "bowl": [ "kitchen", "dining room" ], "coffee mug": [ "kitchen", "home office", "study room" ], "flower pot": [ "living room", "entryway", "bedroom" ], "stool": [ "kitchen", "bedroom", "bathroom" ], "tote bag": [ "entryway", "mudroom", "bedroom" ], "plate": [ "kitchen", "dining room" ], "basket of fruits": [ "kitchen", "dining room", "pantry" ], "vacuum cleaner": [ "living room", "bedroom", "laundry room", "attic", "basement", "mudroom", "walk-in closet" ], "vintage radio": [ "bedroom", "reading nook", "living room" ], "wicker basket": [ "laundry room", "bedroom", "bathroom" ], "jar": [ "kitchen", "pantry" ], "toaster": [ "kitchen" ], "pair of slippers": [ "bedroom", "entryway", "walk-in closet" ], "guitar": [ "bedroom", "living room", "study room", "reading nook" ], "paperweight": [ "home office", "study room" ], "notebook": [ "study room", "home office", "bedroom", "living room" ], "rug": [ "living room", "bedroom", "entryway", "study room" ], "stack of towels": [ "bathroom", "laundry room" ], "alarm clock": [ "study room", "home office", "bedroom" ], "vinyl record player": [ "bedroom", "living room", "reading nook" ], "tray": [ "kitchen", "dining room", "bedroom" ], "plant in pot": [ "living room", "bedroom", "bathroom" ], "air purifier": [ "study room", "home office", "reading nook", "bedroom" ], "suitcase": [ "bedroom", "walk-in closet", "attic" ], "doormat": [ "entryway", "mudroom" ], "blender": [ "kitchen" ], "bean bag": [ "study room", "home office", "living room", "bedroom" ], "shoe rack": [ "entryway", "walk-in closet", "bedroom" ], "knitted pouf": [ "living room", "bedroom", "reading nook" ], "storage bin": [ "laundry room", "attic", "basement" ], "mirror frame": [ "bedroom", "bathroom", "entryway" ], "helmet": [ "living room", "mudroom", "walk-in closet" ], "pair of shoes": [ "entryway", "bedroom", "walk-in closet" ], "basketball": [ "basement", "attic", "mudroom" ], "football": [ "basement", "attic", "mudroom" ], "tennis racquet": [ "basement", "attic", "mudroom" ], "sofa": [ "bedroom", "living room", "study room", "home office", "reading nook" ], "fire extinguisher": [ "entryway", "kitchen", "mudroom" ], "water bottle": [ "study room", "home office", "bedroom", "living room", "kitchen", "dining room" ], "laundry basket": [ "laundry room", "bathroom", "bedroom" ], "pair of gloves": [ "entryway", "bedroom", "mudroom", "walk-in closet", "laundry room" ], "baseball bat": [ "basement", "attic", "mudroom" ], "baseball": [ "basement", "attic", "mudroom" ] }	{ "chair": [ "floor" ], "vase": [ "coffee table", "side table", "dresser top", "fireplace mantel", "entryway table", "wooden table" ], "pillow": [ "ottoman", "bench", "bedside cabinet" ], "blanket": [ "ottoman", "bedside cabinet", "bench" ], "cushion": [ "ottoman", "bench", "bedside cabinet" ], "stack of books": [ "desk surface", "side table", "nightstand", "coffee table", "bookshelf", "wooden table" ], "table lamp": [ "nightstand", "dresser top", "side table", "desk surface", "wooden table" ], "bowl": [ "dining table", "coffee table", "kitchen island", "entryway table", "wooden table" ], "coffee mug": [ "desk surface", "nightstand", "coffee table", "kitchen island", "wooden table" ], "flower pot": [ "window sill", "bookshelf", "entryway table", "dresser top" ], "stool": [ "kitchen island", "bathroom counter" ], "tote bag": [ "entryway table", "bench", "wardrobe top" ], "plate": [ "kitchen island", "dining table", "desk surface" ], "basket of fruits": [ "kitchen island", "dining table", "entryway table" ], "vacuum cleaner": [ "floor", "staircase step" ], "vintage radio": [ "wooden table", "entryway table", "dresser top" ], "wicker basket": [ "bathroom counter", "bench", "entryway table" ], "jar": [ "kitchen island", "bookshelf", "dresser top" ], "toaster": [ "kitchen island" ], "pair of slippers": [ "staircase step", "floor", "bench" ], "guitar": [ "floor", "side table", "entryway table", "ottoman", "wooden table" ], "paperweight": [ "bookshelf", "desk surface", "wooden table" ], "notebook": [ "desk surface", "coffee table", "nightstand", "wooden table", "entryway table", "ottoman" ], "rug": [ "floor" ], "stack of towels": [ "bathroom counter", "wardrobe top" ], "alarm clock": [ "dresser top", "wooden table", "desk surface", "nightstand" ], "vinyl record player": [ "dresser top", "wooden table", "desk surface", "nightstand" ], "tray": [ "kitchen island", "coffee table", "side table" ], "plant in pot": [ "window sill", "side table", "bookshelf", "fireplace mantel" ], "air purifier": [ "floor" ], "suitcase": [ "floor", "bench" ], "doormat": [ "floor" ], "blender": [ "kitchen island" ], "bean bag": [ "floor" ], "shoe rack": [ "floor" ], "knitted pouf": [ "floor" ], "storage bin": [ "wardrobe top", "dresser top" ], "mirror frame": [ "fireplace mantel", "dresser top" ], "helmet": [ "floor", "side table", "entryway table", "ottoman", "wooden table" ], "pair of shoes": [ "floor", "staircase step" ], "basketball": [ "bench", "entryway table", "floor" ], "football": [ "bench", "entryway table", "floor" ], "tennis racquet": [ "bench", "entryway table", "floor" ], "sofa": [ "floor" ], "fire extinguisher": [ "floor" ], "water bottle": [ "desk surface", "side table", "coffee table", "kitchen island", "entryway table" ], "laundry basket": [ "floor" ], "pair of gloves": [ "dresser top", "entryway table" ], "baseball bat": [ "bench", "entryway table", "floor" ], "baseball": [ "desk surface", "coffee table", "bench", "entryway table", "floor" ] }	{ "wooden table": [ "living room", "dining room", "study room", "home office" ], "glass shelf": [ "bathroom", "living room", "entryway", "home office", "bedroom" ], "kitchen island": [ "kitchen" ], "bookshelf": [ "study room", "bedroom", "home office", "reading nook" ], "window sill": [ "bedroom", "living room", "study room", "bathroom" ], "ottoman": [ "living room", "bedroom" ], "side table": [ "living room", "bedroom", "study room" ], "coffee table": [ "living room", "reading nook" ], "nightstand": [ "bedroom" ], "fireplace mantel": [ "living room", "study room" ], "dresser top": [ "bedroom", "walk-in closet" ], "dining table": [ "dining room", "kitchen" ], "desk surface": [ "study room", "home office", "bedroom" ], "wardrobe top": [ "bedroom", "walk-in closet", "attic" ], "bench": [ "entryway", "mudroom", "basement" ], "bathroom counter": [ "bathroom" ], "staircase step": [ "entryway", "basement", "attic" ], "bedside cabinet": [ "bedroom" ], "entryway table": [ "entryway", "mudroom" ], "floor": [ "living room", "bedroom", "kitchen", "dining room", "study room", "home office", "bathroom", "laundry room", "entryway", "reading nook", "walk-in closet", "attic", "basement", "mudroom" ] }	{ "A": [ "{a_object1} {object1} placed on {a_surface} {surface} in {a_scene} {scene}.", "A single {object1} resting on {a_surface} {surface} in {a_scene} {scene}.", "{a_object1} {object1} located on {a_surface} {surface} in {a_scene} {scene}." ], "B": [ "{a_object1} {object1} beside {a_object2} {object2} on {a_surface} {surface} in {a_scene} {scene}.", "{a_object1} {object1} and {a_object2} {object2} placed on {a_surface} {surface} in {a_scene} {scene}.", "{a_object1} {object1} located next to {a_object2} {object2} on {a_surface} {surface} in {a_scene} {scene}." ], "C": [ "{a_object1} {object1}, {a_object2} {object2}, and {a_object3} {object3} placed on {a_surface} {surface} in {a_scene} {scene}.", "A group of objects including {a_object1} {object1}, {a_object2} {object2}, and {a_object3} {object3}, arranged on {a_surface} {surface} in {a_scene} {scene}.", "{a_object1} {object1}, {a_object2} {object2}, and {a_object3} {object3} are resting on {a_surface} {surface} in {a_scene} {scene}." ], "D": [ "{a_object1} {object1}, {a_object2} {object2}, {a_object3} {object3}, and {a_object4} {object4} placed on {a_surface} {surface} in {a_scene} {scene}.", "An arrangement of {a_object1} {object1}, {a_object2} {object2}, {a_object3} {object3}, and {a_object4} {object4} on {a_surface} {surface} in {a_scene} {scene}.", "A group of four objects — {a_object1} {object1}, {a_object2} {object2}, {a_object3} {object3}, and {a_object4} {object4} — positioned on {a_surface} {surface} in {a_scene} {scene}." ] }

Dataset Card for Prompt2SceneBench

Dataset Details

Dataset Description

Prompt2SceneBench is a structured prompt dataset with 12,606 text descriptions designed for evaluating text-to-image models in realistic indoor environments. Each prompt describes the spatial arrangement of 1–4 common household objects on compatible surfaces and in contextually appropriate scenes, sampled using strict object–surface–scene compatibility mappings.

Curated by: Bodhisatta Maiti
Funded by: N/A
Shared by: Bodhisatta Maiti
Language(s): English
License: CC BY 4.0

Dataset Sources

Uses

Direct Use

Prompt2SceneBench can be directly used for:

Prompt-to-image generation using models like Stable Diffusion XL to benchmark compositional accuracy in indoor scenes.
Prompt–image alignment scoring, evaluating how well generated images match the structured prompts.
Compositional generalization benchmarking, testing models on spatial arrangement of 1–4 objects with increasing difficulty.
Zero-shot captioning evaluation, using prompts as pseudo-references to measure how captioning models describe generated images.
Scene layout reasoning tasks, e.g., predicting spatial configuration or scene graph generation from textual prompts.
Style transfer or image editing tasks,where the structured prompt can guide object placement or scene modification in indoor contexts.
Multimodal fine-tuning or distillation, where paired structured prompts and generated images can be used to improve alignment in vision-language models (VLMs), especially for grounding objects, spatial relationships, and indoor scene context.
Controllable generation studies, analyzing prompt structure impact on generated outputs under different text-to-image models.

Out-of-Scope Use

Outdoor scenes, surreal or abstract visual compositions.
Benchmarks involving human-centric understanding or motion.
Direct use for safety-critical or clinical systems.

Dataset Structure

CSV Format (`prompt2scene_prompts_final.csv`)

Size: 12,606 prompts

Each row in the CSV corresponds to a single prompt instance and includes the following fields:

type: Prompt category — one of A, B, C, or D, based on number of objects and complexity.
object1, object2, object3, object4: Objects involved in the scene (some may be None/NaN/Null depending on type).
surface: The surface where the objects are placed (e.g., desk surface, bench).
scene: The indoor environment (e.g., living room, study room).
prompt: The final structured natural language prompt.

Note:

Type A prompt has only 1 object (object2, object3, object4 fields will be None/NaN/Null)
Type B prompt has only 2 objects (object3, object4 fields will be None/NaN/Null)
Type C prompt has only 3 objects (object4 field will be None/NaN/Null)
Type D prompt has 4 objects (all the object fields will have values)

Sample Examples:

Type A: a football located on a bench in a basement. (object1: football, surface: bench, scene: basement)
Type B: a coffee mug beside a notebook on a wooden table in a home office. (object1: coffee mug, object2: notebook, surface: wooden table, scene: home office)
Type C: a jar, a coffee mug, and a bowl placed on a kitchen island in a kitchen. (object1: jar, object2: coffee mug, object3: bowl, surface: kitchen island, scene: kitchen)
Type D: An arrangement of an air purifier, a pair of slippers, a guitar, and a pair of shoes on a floor in a bedroom. (object1:air purifier, object2: pair of slippers, object3: guitar, object4: pair of shoes, surface: floor, scene: bedroom)

JSON Format (`prompt2scene_metadata.json`)

The JSON contains the following keys:

objects: List of all 50 objects used in the prompt generation.
scenes: List of 15 indoor scenes.
surfaces: List of 20 compatible surfaces.
object_to_scenes: Dictionary mapping each object to plausible indoor scenes.
object_to_surfaces: Dictionary mapping each object to compatible surface(s).
surface_to_scenes: Dictionary mapping each surface to scene(s) where it naturally occurs.
prompt_templates: Template used for generating the prompts for all the prompt types (A, B, C and D), each prompt type has 3 variants

This JSON file supports reproducibility and reuse by providing all internal mappings used during structured prompt generation. The community can further extend/modify the above lists and mappings and use their own prompt templates based on the usecase.

Dataset Creation

Curation Rationale

The dataset was created to provide a controlled and structured benchmark for evaluating spatial and compositional understanding in generative AI systems, particularly in indoor environments.

Source Data

Data Collection and Processing

All data is programmatically generated using a controlled sampling routine from curated lists of 50 indoor objects, 20 surfaces, and 15 scenes. Only valid object–surface–scene combinations were retained using rule-based mappings.

Who are the source data producers?

The dataset is fully synthetic and was created by Bodhisatta Maiti through controlled generation logic.

Annotations

No human annotations are involved beyond the original curation and sampling logic.

Personal and Sensitive Information

No personal or sensitive information is present. The dataset consists of entirely synthetic prompts.

Bias, Risks, and Limitations

This dataset focuses only on physically and contextually plausible indoor scenes. It excludes unusual, humorous, or surrealistic scenarios intentionally. It may not cover the full range of compositional variation needed in creative applications.

Recommendations

Use with generative models that understand object placement and spatial grounding. Avoid using it to benchmark models trained for outdoor or abstract scenes.

Citation

APA:

Maiti, B. (2025). Prompt2SceneBench: Structured Prompts for Text-to-Image Generation in Indoor Environments [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15876129

Glossary

Type (Prompt category): The number of objects (1 to 4) described in the scene vary based on the prompt type (A, B, C and D).
Surface: Physical platform or area where objects rest.
Scene: Room or environment in which the surface is situated.