| ---
|
| pretty_name: VPhotoBench
|
| license: other
|
| language:
|
| - en
|
| size_categories:
|
| - 100<n<1K
|
| tags:
|
| - blender
|
| - 3d
|
| - virtual-photography
|
| - vision-language-models
|
| - camera-control
|
| - agentic-ai
|
| configs:
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| - config_name: tasks
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| data_files:
|
| - split: train
|
| path: benchmark/tasks.csv
|
| - config_name: scenes
|
| data_files:
|
| - split: train
|
| path: benchmark/scenes.csv
|
| - config_name: attribution
|
| data_files:
|
| - split: train
|
| path: licenses/ATTRIBUTION.csv
|
| - config_name: licenses
|
| data_files:
|
| - split: train
|
| path: licenses/LICENSES_PER_ASSET.csv
|
| - config_name: benchmark_list
|
| data_files:
|
| - split: train
|
| path: metadata/benchmark_list_clean.csv
|
| ---
|
|
|
| # VPhotoBench
|
|
|
| VPhotoBench is a language-conditioned virtual photography benchmark built from 47 open-license Blender scenes. Each scene is paired with three photography missions: subject placement, relational composition, and atmosphere/style. This release contains 47 Blender `.blend` scene files and 141 English task prompts.
|
|
|
| ## Contents
|
|
|
| - Scenes: 47
|
| - Tasks: 141
|
| - Mission types: subject placement, relational composition, atmosphere/style
|
| - Total Blender asset size: 6.34 GiB
|
| - Per-asset licenses: CC-BY: 22, CC-BY-ND: 1, CC-BY-SA: 12, CC0: 12
|
|
|
| ## Directory structure
|
|
|
| ```text
|
| assets/blender/ # 47 Blender scene files, one directory per scene
|
| benchmark/scene_registry.json # Scene metadata and package-relative file paths
|
| benchmark/scene_registry_v1.json # Runner-compatible scene registry
|
| benchmark/tasks.json # 141 task prompts
|
| benchmark/tasks_v1.json # Runner-compatible task registry
|
| benchmark/split_v1.json # Canonical dev/test split used by the paper scripts |
| benchmark/tasks.csv # Flat task table |
| evaluation/EVALUATION_PROTOCOL.md # How to score rendered images with UniPercept |
| evaluation/run_unipercept_scoring.py # UniPercept-based image scoring helper |
| metadata/resource_manifest.json # File sizes, checksums, and package paths |
| metadata/checksums_sha256.txt # SHA256 checksums for all packaged .blend files |
| metadata/benchmark_list_original.csv # Original source table
|
| metadata/benchmark_list_original.xlsx # Original source spreadsheet, when present
|
| licenses/ATTRIBUTION.csv # Per-scene TASL-style attribution table
|
| licenses/ATTRIBUTION.md # Human-readable attribution summary
|
| licenses/LICENSES_PER_ASSET.csv # Per-scene license table
|
| scripts/validate_package.py # Local package integrity checker
|
| ```
|
|
|
| ## Usage
|
|
|
| The task files use package-relative paths. A task entry points to a `scene_id`; the corresponding scene entry gives the `.blend` file path under `assets/blender/`.
|
|
|
| ```python
|
| import json
|
| from pathlib import Path
|
|
|
| root = Path("VPhotoBench")
|
| scenes = {s["scene_id"]: s for s in json.loads((root / "benchmark/scene_registry.json").read_text())["scenes"]}
|
| tasks = json.loads((root / "benchmark/tasks.json").read_text())["tasks"]
|
|
|
| task = tasks[0]
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| blend_file = root / scenes[task["scene_id"]]["blend_file"]
|
| print(task["task_id"])
|
| print(blend_file)
|
| print(task["instruction"])
|
| ```
|
|
|
| JSON files are the authoritative task and scene registries. CSV files are
|
| provided for convenience and are encoded as UTF-8.
|
|
|
| ## Evaluation |
|
|
| VPhotoBench does not require a built-in evaluator inside the Blender assets. |
| After a method renders final images for the task prompts, users can score those |
| images with UniPercept: |
|
|
| https://github.com/thunderbolt215/UniPercept |
|
|
| We provide a small helper in `evaluation/run_unipercept_scoring.py`. Prepare a |
| CSV file with `task_id,image_path`, then run: |
|
|
| See `evaluation/EVALUATION_PROTOCOL.md` for details. The helper reports |
| UniPercept `IAA`, `IQA`, and `ISTA` scores, plus an optional default composite |
| `M_qs = 0.40 * IAA + 0.20 * IQA + 0.40 * ISTA`. |
|
|
| ## Licensing and attribution
|
|
|
| The benchmark is a metadata and task release over third-party Blender assets. The dataset as a whole does not impose a single blanket license over all assets. Each `.blend` file remains under its upstream license. See:
|
|
|
| - `licenses/LICENSES_PER_ASSET.csv`
|
| - `licenses/ATTRIBUTION.csv`
|
| - `licenses/ATTRIBUTION.md`
|
|
|
| For Creative Commons assets, this release follows a TASL-style attribution table where available: title, author, source URL, and license. Some upstream entries do not have a stable source URL in the local project records; these rows are explicitly marked in `attribution_status`. Current unresolved or partial attribution rows: 0.
|
|
|
| Users are responsible for checking each upstream license before redistribution or commercial use. In particular, rows marked as fan art or partial attribution should be reviewed against the upstream source.
|
|
|
| ## Dataset construction
|
|
|
| The 47 scenes were selected from public Blender scene repositories and paired with three natural-language photography instructions per scene. Each instruction asks an agent to choose an executable camera state in the original 3D scene rather than generate a new image.
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @misc{guo2026photoflowagentic3dvirtual,
|
| title={PhotoFlow: Agentic 3D Virtual Photography Missions},
|
| author={Jiarui Guo and Haojia Wei and Yiming Zhang and Yifei Liu and Yuning Gong and Hongjie Zhang and Xue Yang and Zhihang Zhong},
|
| year={2026},
|
| eprint={2605.23771},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.CV},
|
| url={https://arxiv.org/abs/2605.23771},
|
| }
|
| ```
|
|
|
