PICABench: How Far Are We from Physically Realistic Image Editing?
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.
68f6ed0724c448936311184f
Yuandong Pu
claimed_verified
2025-10-21T14:03:54.052Z
625d5b9f0bec31f086e04cd9
https://cdn-avatars.huggingface.co/v1/production/uploads/1650285458447-noauth.jpeg
YuandongPu
user
Andrew613
68f6ed0724c4489363111850
Le Zhuo
68f6ed0724c4489363111851
Songhao Han
claimed_verified
2025-10-21T14:03:50.886Z
662885b9b87483ae5a9ee5c9
https://cdn-avatars.huggingface.co/v1/production/uploads/662885b9b87483ae5a9ee5c9/fLzCrWizo_hy7zGuAqFwk.jpeg
Songhao Han
user
hshjerry0315
68f6ed0724c4489363111852
Jinbo Xing
68f6ed0724c4489363111853
Kaiwen Zhu
68f6ed0724c4489363111854
Shuo Cao
68f6ed0724c4489363111855
Bin Fu
68f6ed0724c4489363111856
Si Liu
68f6ed0724c4489363111857
Hongsheng Li
68f6ed0724c4489363111858
Yu Qiao
68f6ed0724c4489363111859
Wenlong Zhang
68f6ed0724c448936311185a
Xi Chen
68f6ed0724c448936311185b
Yihao Liu
PICABench: How Far Are We from Physically Realistic Image Editing?
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.
PICABench and PICAEval evaluate physical realism in image editing by assessing eight sub-dimensions and using VLM-as-a-judge with human annotations, highlighting the need for physics-based solutions.
PICABench PICAEval VLM-as-a-judge physical realism image editing physics-based solutions PICA-100K