README.md

---

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:
  - config_name: tasks
    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]

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},

}

```