PICABench: How Far Are We from Physically Realistic Image Editing?
Authors: name: Yuandong Pu, name: Le Zhuo, name: Songhao Han, name: Jinbo Xing, name: Kaiwen Zhu, name: Shuo Cao, name: Bin Fu, name: Si Liu, name: Hongsheng Li, name: Yu Qiao, name: Wenlong Zhang, name: Xi Chen, name: Yihao Liu

Image editing has achieved remarkable progress recently. Modern editing
models could already follow complex instructions to manipulate the original
content. However, beyond completing the editing instructions, the accompanying
physical effects are the key to the generation realism. For example, removing
an object should also remove its shadow, reflections, and interactions with
nearby objects. Unfortunately, existing models and benchmarks mainly focus on
instruction completion but overlook these physical effects. So, at this moment,
how far are we from physically realistic image editing? To answer this, we
introduce PICABench, which systematically evaluates physical realism across
eight sub-dimension (spanning optics, mechanics, and state transitions) for
most of the common editing operations (add, remove, attribute change, etc). We
further propose the PICAEval, a reliable evaluation protocol that uses
VLM-as-a-judge with per-case, region-level human annotations and questions.
Beyond benchmarking, we also explore effective solutions by learning physics
from videos and construct a training dataset PICA-100K. After evaluating most
of the mainstream models, we observe that physical realism remains a
challenging problem with large rooms to explore. We hope that our benchmark and
proposed solutions can serve as a foundation for future work moving from naive
content editing toward physically consistent realism.