Using UniPercept for VPhotoBench Outputs
VPhotoBench provides Blender scenes and text prompts. After running an agent or camera-search method, users can evaluate the final rendered images with UniPercept:
https://github.com/thunderbolt215/UniPercept
UniPercept returns three perceptual scores for each image:
IAA: image aesthetics assessment.IQA: image quality assessment.ISTA: image structure and texture assessment.
1. Prepare Results
Create a CSV file with one row per rendered result:
task_id,image_path
001_blender_4_0_splash_subject_placement,images/001_blender_4_0_splash_subject_placement.png
task_id should match the IDs in benchmark/tasks.json. image_path can be
absolute or relative to the submission/result directory.
2. Install UniPercept
Follow the official UniPercept instructions. The lightweight package interface can be installed with:
pip install unipercept-reward
If you use a local checkpoint or a specific UniPercept release, follow the setup steps in the UniPercept repository.
3. Score Images
Run:
python evaluation/run_unipercept_scoring.py \
--benchmark-root . \
--submission path/to/submission.csv \
--submission-root path/to/result_directory \
--output path/to/unipercept_scores.csv \
--device cuda
The output CSV contains:
task_id,scene_id,mission_type,image_path,status,iaa,iqa,ista,m_qs,succ_at_0_55,error
The script normalizes UniPercept scores from 0-100 to 0-1. It also reports
an optional composite score:
M_qs = 0.40 * IAA + 0.20 * IQA + 0.40 * ISTA
and:
Succ@0.55 = 1 if M_qs >= 0.55 else 0
These two derived fields are provided only as a convenient default summary. Users may report the three UniPercept scores directly or define their own aggregation rule for a specific study.