AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

OpenCompass Hub

PICABench

Whether an image edit also updates the physical consequences of the change.

Cited research guide; original source measurements remain independent.

Beyond following the edit instruction

Removing an object should also update its shadow, reflections and interactions. The benchmark covers optics, mechanics and state changes.

PICABench — original paper

How PICAEval checks an edit

A vision-language judge answers spatially anchored questions using human-annotated regions. Inspect region-level questions alongside the overall accuracy.

PICABench — original paper

Keep training and evaluation apart

PICABench contains evaluation cases; PICA-100K is a separate training resource derived from video. They serve different purposes.

PICABench — authors’ project

Read results in context

Keep the editing model, output resolution and judge configuration with any score. The authors’ repository links the leaderboard and reproducible evaluation pipeline.

PICABench — original paper

Evidence available for this benchmark

Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.

Start with these resources

Cited documents and dates (1)

Catalog collected on 2026-08-18; resources and fields reflect that saved source.

Evaluation context and evidence

Source
OpenCompass Hub
Publisher
Shanghai AI Laboratory
Benchmark release
2025-10-27
Collected
2026-08-18
Modality
multimodal

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 0

This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.