PICABench: How Far are We from Physical Realistic  Image Editing?
Authors: Yuandong Pu
Le Zhuo
Songhao Han
Jinbo Xing
Kaiwen Zhu
Shuo Cao
Bin Fu
Si Liu
Hongsheng Li
Yu Qiao
Wenlong Zhang
Xi Chen
Yihao Liu
Venue: ICLR 2026 Poster (2026)
Keywords: image edit; benchmark; dataset
Primary area: datasets and benchmarks

Abstract: 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.

Published as a conference paper at ICLR 2026
PICABENCH: How FAR ARE WE FROM PHYSI- CALLY REALISTIC IMAGE EDITING?
Yuandong Pul:2 Bin Fu²
Le Zhuo
Songhao Han
Jinbo Xing
Kaiwen Zhu
Shuo C
Si Liu
Hongsheng Li
Yu Qiao²
Wenlong Zhang²
Xi Chen Sf
Yihao Liu
Shanghai
Jiao Tong University
Shanghai Al Laboratory
*CUHK MMLab
Krea Al
Beihang University
Tongyi Lab
USTC 8 Thhe University of Hong Kong
ABSTRACT
Image editing has achieved remarkable progress recently. Modern editing mod- els could already follow complex instructions manipulate the original content. However, beyond completing the editing instructions, the accompanying physical effects are the key 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 com- pletion but overlook these physical effects. So, 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 (span- ning optics, mechanics, and state transitions) for most 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, 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 foundation for future work moving from naive content editing toward physically consistent realism.
also explore effective
Project page: tttss//iccarescarche github.oo
INTRODUCTION
Recent advances instruction based image editing have brought remarkable progress (Wu et al. 2025a Batifol et al. 2025 OpenAl 2025b; Google [2025] Seedream et al.) [2225] Liu al. 2025
Cai et al. 2025). In particular, lar, with the emergence of unified multi-modal models Deng et al. 2025
Lin et al. 2025 Wu al. 2025b), they seamlessly follow natural language instructions and produce visually compelling, semantically coherent edits. These systems have demonstrated strong generalization capabilities across diverse domains, establishing new standard for controllable and high-quality image manipulation.
However, the realism of image editing depends not only on semantic accuracy but also the correct rendering of physical effects. Even simple operations like object addition or removal often trigger complex interactions with lighting, shadows, and object support in the scene. Existing benchmarks overlook this limitation by solely emphasizing semantic fidelity and visual consistency. A Although some recent benchmarks (Wu et al. 2025cc Li al., 2025) attempt to probe scientific-plausible editing capabilities, their test cases diverge from common user-edit scenarios but focus scientific domains with specific physical or chemistry knowledge. Consequently, we lack a clear understanding of how far are from physically realistic image editing.
"Tii work was done during his internship at Shanghai Artificial Intelligence Laboratory. 1 Corrrsponning Authors

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Published as a conference paper at ICLR 2026
OPTICS
Benose side
Meve the left
OFt.lmage-
Remove
Sextrem 4D
Remove
Bapel
State Transiton
Freeza the apple
Stapik-60n
Change to surey day
Ohange samner
Muckanics
Adc Centioal
Ceett- Imagee-e--
Serine
Flar1 Koeleat
Figure I: Challenging cases from Bench. Despite providing instraction-aligned outputs, current SoTA models still struggle with generating physically realistic edits, resulting unharmonized lighting, deformation, or state transitions with common editing operations.
To address this gap, we introduce PICA (PhysICs- Aware) Bench-a a diagnostic benchmark designed to evaluate physical realism in image editing beyond semantic fidelity. Drawing on common require- ments in real-world editing applications Taesiri al. (2025), categorize physical consistency into three intuitive dimensions that are often overlooked in typical editing tasks: Optics, Mechan- ics, and State Transition. These dimensions were selected to reflect common but under-penalized error types, such as unrealistic lighting effects, impossible object deformations, or implausible state changes. Together, they span eight sub-dimensions, each defined by concrete, checkable criteria: Optics includes light propagation, reflection, refraction, and light-source effects; Mechanics captures deformation and causality; and State Transition addresses both global and local state changes. This fine-gra ine-grained taxonomy facilitates systematic assessment of whether edited images adhere to principles such as lighting consistency, structural plausibility, and realistic state transitions. Together, it enables comprehensive evaluation and targeted diagnosis of physics violations in image editing models.
With the carefully curated test cases, evaluating the physical correctness remains challenging. We introduce PICAEval, reliable and interpretable protocol tailored for physics-award assessment. While existing VLM-as-Judge setups (Wu et al., 2025c Niu et al.) 2025| [Sun et al.) 2025| Zhao al. 2025) offer convenient way automate evaluation, they typically rely general prompts without grounding in physical principles. As a result, these setups often lack sensitivity to nuanced physical violations and may produce hallucinated judgments when faced with subtle localized cues. Facing this challenge, PICAEval adopts targeted, per-example Q&A aligned with specific physical sub-dimensions, substantially improving diagnostic accuracy. To further reduce hallucination, incorporate grounded Auman-annotated key regions (e.g.. reflection surfaces, contact interfaces). directing the model's 's attention to physically relevant evidence. This protocol yields high agreement with human assessments, offering reliable measurement for physical correctness.
Beyond evaluation, we provide strong baseline by learning physics from videos. Specifically, we present PICA-100K, synthetic dataset of 100k editing examples constructed from videos. Prior work Yu et al. 2025b Chen al. 2025 Chang et al., 2025 Cao et al. 2025a) has shown that editing pairs derived from videos can enhance the quality and robustness of editing models. Motivated by recent advances in video generation approaching world-simulator (Wan et al., 2025). we design an automatic pipeline that integrates text-to-image model as scene renderer and image-to tonge-to-video model as a state- treesstraniiionnn simulator. From the generated videos, we extract temporally coherent editing pairs and further recalibrate multi-level editing instructions using GPT-5. Our experiments
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Published as a conference paper at ICLR 2026
shows that finetuning on PICA-100K significantly improves the baseline model's capability to generate physically realistic editing results without sacrificing semantic quality.
We benchmark 11 open- and closed-source image editing models across diverse architectures and scales. PICABench comprchensively distinguishes models based on their level of physical awareness, while PICA- effectively improves model performance. As shown in Fig. [ modeling physical realistic transformations is still challenging for current SoTA models, which underlines the signifi- cance of advancing from semantic editing toward physically grounded image manipulation in the future. Our main contributions could be summarized as follows.
We introduce PICABench, comprehensive and fine-grained benchmark for physics- image editing. It covers diversified physical effects (eight sub-dimensions) and includes the great majority of commonly required editing operations in practical applications.
We propose PvCA region- aaarr, VQA-based evaluation protocol that incorporates numan-annotated key regions to provide interpretable and reliable assessments for physical correctness, improving robustness to subtle errors compared to general scoring prompts.
We construct PICA- a large-scale dataset derived from synthetic videos, and show that fine-tuning existing models (e.g. FLUX.1 Kontext) on this dataset effectively enhances their physical consistency while preserving semantic fidelity.
RELATED WORK
2.1 INSTRUCTION- IMAGE EDITING MODELS
Recent advances in instruction-base based image editing have led to substantial progress in controllable
and diverse visual manipulation YYe et al. 2025a Yu al. 2025a Zeng al. 2025 Jin al. 2024
Huang et al.] [2224] Krojer et al.] 2024)) Prior approaches implement image editing in a training Freee
manner Yang et al.) |023] Pan al. [202]] Couairon et al. 2022). Recent training-bases methods
such as HiDream-Ein E1.1 (Cai et al. (025). StepIX-Edit (Liu et al. 2025), FLUX.1 Kontext (Batifol al. 2025), and
(Wu al., 2025a) improve edit quality, responsiveness, and instruction alignment, while unified frameworks (e.g., Bagel (Deng et al.) (025), OmniGen2 (Wu ct al. 2025b), UniWorld-Vi (Lin et al., 2025)) integrate instruction- -oolloiin. visual reasoning, and multi-task learning to support diverse tasks like free-form manipulation, future-frame prediction, multiview synthesis, segmentation, and composition. Closed-source systems (e.g., GPT- Image-
1 (OpenAT 2025b), Seedream 4.0 (Seedream et al., 2025). Nano Bana-aa (Google 2025) further demonstrate strong user-intent alignment and high visual fidelity across text-to-image and image-to- image workflows. However, despite these gains, most approaches prioritize semantic and perceptual quality and often neglect physical constraints, leading to artifacts such as unrealistic shadows, refractions, and deformations, underscoring the need for physics-aware editing.
2.2 INSTRUCTION- BASSE IMAGE EDITING BENCHMARKS
Instruction-based image editing benchmarks have evol evolved from early reliance on semantic (DINO,
CLIP (Zhang et al. 2023 Wang et al. 2023 Ma et al., 2024)) and pixcl-level metries, which capture
similarity but miss fine- grained semantic alignment, to modern
"LM-as-a-Judge evaluations (Wu
al. 2025c Niu et al. 2025 Zhao et al., 2225) Sun et al. 2025: Ye et al. 20256] Liu al. 2025
Cao et al. 2025b Zhuo et al. 2025) that use lasion-language models to rate instruction adherence, perceptual quality, and realism across diverse, complex prompts. While these L..M-based approaches enable general dineeiioamnii scoring, they are prone to overlooking physically implausible edits (e.g., unrealistic lighting, deformations, or object interactions) and can hallucinate, allowing visually appealing yet inconsistent outputs to score well. To close this gap, we introduce physics- aware benchmark and the PICAEval a
QA-based metric that evaluates physical consistency through localized, interpretable assessments anchored specific regions of interest.
3 METHOD
In this section, we first give an overall introduction of PICABench, a benchmark structured to evaluate physical realism in image editing. We then dive into the construction steps, begin with
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Published as a conference paper at ICLR 2026
Glsol State Trassioon
Supe ficalt Aug, Lenger revern stanes Aug. Lengte $2.6 Eeplict: Aug. sangth 101.2
Mecianica
Local Store Trasshor
ILTTC
Optics
Pecoetoge
Defermanor
0.0%
Beflechon
16.4%
Light Propggeion
D0G
Siote Tranditon
Light Source Effects
Refraction
Wood
(a) Distribution of qa pains
(b) Word length distribution of instruction
(6) Distribution of PICABanch
Figure 2: Statistics Analysis of PICABench. PICABench is comprehensive benchmark designed to evaluate physical realism of image editing models across eight sub-dimentinons. Fig. 2[a) shows distribution of QA pairs. Fig. 2(b) presents words length distribution of editing instruction across
three levels of prompt. Fig. 2 provides a perspective on overall composition of PICABench.
the data curation pipeline. which pairs diverse images with multi-level editing instructions. Next, we present PICAEval, a gggion-grouneed evaluation protocol for reliable assessment. Finally, we propose PICA-I00K, synthetic dataset built from videos, and show how fine-tuning provides strong baseline for improving physics-awan aware editing.
3.1 PICABENCH
We introduce the task coverage and overall statistics of PICABench. Our benchmark focuses on three core dimensions of physical realism: Optics, Mechanics, and State Transition, which reflect common yet overlooked failure modes such as unrealistic lighting, implausible deformations, and invalid state changes. As shown in Fig. Fig.Z(c) and Fig. 3 the benchmark includes 900 editing samples spanning these three dimensions. further divided into eight sub-dimensions with concrete and checkable criteria-ranginn from optical effects, mechanical plausibility, and realistic state transitions.
Optics. This category evaluates whether edited images follow the basic physical rules of light, including how casts shadows, reflects from surfaces, bends through transparent materials, and interacts with light sources. Edits should produce shadows, reflections, refractions, and light-source effects that align with the scene's 's geometry and lighting-matching shadow direction and occlusion, enabling view- and shape-dependen reflections, ensuring smooth background distortion through transparent media, and maintaining consistent color, softness, and falloff for added light sources. These effects, while often subtle, are key to making edits appear natural and physically believable.
Mechanics. This category evaluates whether edited objects remain mechanically and causally consistent with the scene. Deformation should follow material properties-righe objects must retain shape, while elastic ones deform smoothly with consistent texture and geometry. Causality covers a broader range of physically plausible effects, including structural responses to force redistribution, agent reactions to added or removed stimuli, and environmental changes that alter object behavior, all of which must follow consistent physical behavioral laws.
State transition. This category evaluates whether environmental and material changes unfold in a physically coherent manner, either across the entire scene within localized regions. Global state transitions, such as changes in time of day, season, or weather, must update all relevant visual cues consistently-raaginnn from lighting and shadows vegetation, surface conditions, and atmospheric effects. These changes require coordinated, scene-wide modifications that follow natural temporal or environmental progression. Local state transitions, on the other hand, involve targeted physical changes confined to specific objects or regions. These include phenomena such as wetting, drying, melting. burning, freezing, wrinkling, splashing, or fracturing. Edits must integrate smoothly with surrounding context, preserve material boundaries, and maintain plausible causal triggers.
3.2
DATA CURATION
To enable reliable, fnn-grained evaluation of physically realistic image editing, we curate benchmark entries that pair natural images with editing instructions explicitly designed to test physical consistency. Our data curation pipeline aligned with the taxonomy Sec. [.1 and structured into two stages: Data Collection and Edit Instruction Construction. A visual overview is shown in Fig. 4
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Reflection
Global State Transition
red corgo ship? YES
the Imrge? At: YES
reflection
GP the ferm bright yreen the image? A2 Yos
The in the inage? A2:
Editing Instruction: Move the ship to the right
Editing Instruction: Chonge to summer
Light Propagation
Local State Transition
Q: Ia thene a ise NO
Qt: thene vibble ice maje?
YES
Q2 there cone shoped
QI theee visille ice
Editing Instruction: cream
Editing Instruction: Freeze the
Light Source Effects
Deformation
dog
ATHES
AtNO
groy outted Insentutios? al: Year
AZYES
Editing Instruction: Turn the lamp the image
Editing Instruction: Remove dog
Reflection
Reflection
Qo crea showd ditarrion? A1MES
Qui Is the shatdboord abcent?
Are shadows from the clsss cups gane?
Q2 la the shoe touching the povemant?
Editing Instruction: Remove all the glasses
Editing Instruction: Remove the skate board
Figure 3: Overview of Bench. We present illustrative examples from eight sub-dimensions. Key regions are annotated to help reduce hallucination for VLMs.
Data collection. We begin by defining structured vocabulary mapped to the eight sub-diueeeee uub-dimensions. To broaden the coverage, we use GPT-5 to expand this vocabulary into a rich keyword set encompass- ing materials, lighting contexts, and long-tail phenomena. We then these keywords retrieve candidate images from licensed and public sources. We prioritize visually diverse scenes that exhibit salient physical cues, such directional lighting, transparent or reflective media, deformable objects, or phase- changeebe substances. Human annotators filter duplicates and artifacts and tag applicable sub-dimensions for each image support subsequent annotation.
Instruction construction. Each retained image is paired with human- natural language instruction that induces a physics-relevant edit, grounded in the scene's 's physical affordances and designed to implicitly target a specific uub-dimension. To assess not only whether models can follow surface-level commands but also whether they can internalize and apply physical knowledge construct three levels of instruction complexity: superficial
under varying prompt conditions, prompts that issue plain edit commands without explanations, which probe models' intrinsic physical priors and align with realistic usage scenarios: intermediate prompts that include brief rationale grounded in physical rules, blueserving as reasoning cues to activate physical knowledge; and explicit prompts that further describe the expected results of the edit, minimizing ambiguity to strictly assess visua capabilities. We use GPT-5 to expand each human-authored instruction into these three forms, followed by manual review ensure clarity, factual correctness, and alignment with the visual context. For each sample, the benchmark retains a canonical version of the instruction.
3.3 PICAEVAL
Evaluating physically realistic image editing remains challenging. Unlike semantic fidelity or perceptual quality, physical realism is inherently contextual: it depends not only on the edited content but also on its alignment with the physical constraints implied by the original scene and instruction. Moreover, there is no reference image to serve as ground truth, and general prompting strategies such "Is this edit correct?" often yield vague hallucinated responses from VLMs.
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(a) Enrich Keywords
(b) Collection & Review
(c) Instruction Annotation
dondelion
Shadow
fpt Weather
Set the
Coca-Cola
environment negative
Berosss flowrs and keep the wote
Reflection
GPT-5
Instruction Refinement
(e) Edit Region Annotation
(f) Q4A Annotation
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According the image and editing instruction, design QAA,
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Expected
Output
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Figure 4: Overall pipeline for benchmarks construction and evaluation. (a-b) We enrich physics-specific keyword set and retrieve diverse candidate images. (c-d) Human- written editing instructions expanded into three levels of complexity using GPT-5. (c) Annotators mark physics- critical regions. (f) Spatially grounded yes/no questions are generated to evaluate physical plausibility, (g) During evaluation, VLMs answer each question with reference to the edited region.
To address this, we introduce PICAEval, a aegion-grrunded. guestion-answering based metric designed assess physical realism modular, interpretable manner. Inspired by recent work (Chai et al. 2024 Han et al. ).25), PICAEval decomposes each evaluation instance into multiple region- specific verification questions that can be reliably judged by a VLM. Each benchmark entry paired with curated set of spatially grounded yes/no questions designed to probe whether the edited image preserves physical plausibility within key regions. These questions are tied observable physical phenomena such as shadows, reflections, object contact, material deformation and are anchored to Human--annotatee regions of interest (ROIs). This design encourages localized, evidence- basee reasoning and reduces the influence of irrelevant image content the VLM's judgment.
Evaluation pipeline, As illustrated Fig. 4c-D) the evaluation proceeds as follows: (I) Annotators mark key regions in the input image where physics-cne physics-critical evidence expected appear post-editing (e.g., reflective surfaces, deformation zones, cast shadows); (2) Using the edit instruction and region, GPT-5 generates set of 4-5 binary QA pairs per entry, which are then manually reviewed for clarity and coverage; (3) At test time, a VLM (e.g., GPT-5) is prompted with the edited image, instruction, region, and question, and produces
constrained to the visible content within the region.
PICAEval is computed as the proportion of questions for which the VLM answer exactly matches the reference label. Compared direct prompting. this QA-based protocol offers three key advantages: (i) spatial grounding reduces hallucination, (ii) decomposition increases interpretability and robustness, and (iii) the format better mirrors how humans evaluate physical plausibility -ttroghh concrete, localized checks. We report quantitative comparisons and per-subdimension breakdowns to enable diagnostic analysis of physics-aware image editing capabilities Sec.
3.4 STRONG BASELINE: LEARNING PHYSICAL REALISM FROM VIDEOS
To address the limitations identified in Sec. 3.1 we introduce PICA-100K, a purely synthetic dataset designed to improve physics-aware image editing. Our decision to fully generated data driven

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Published as a conference paper at ICLR 2026
Prompts Curation
Sceme Prompis
AUUUX
Wan
Source
Target
Collection
a. teapot
T21 prompt
12V promp
Sood Prompts Set
GPT-5 Refinement
L
Objects Library
Edit Instruction: Remove the green kettle from the stovetop
Final Prompts
construction
phompt
Figure 5: PICA-100K construction pipeline. We first curate structured prompts for scene and subject composition, refined by GPT-5 and rendered using L.UXXI-Krea-des for text-to-image generation. Motion-based edit instructions are created via GPT-5 and applied using Wan2.2-14B to synthesize short videos depicting physical transformations.
by three primary motivations. First, prior work (Yu al. 2025b Chen et al. 2025 Cao al. 2025a Chang et al. 2025) has demonstrated that constructing image-editing data from video is an effective strategy for enhancing model performance, particularly for capturing real world dynamics. Second, building large-scale, real-worid datasets tailored to physics-aware editing is prohibitively expensive and possibilities: state-of-the-an text-to-image models (Labs 2024) now generate highly realistic and diverse images, while powerful image-to-video (12V) models such as Wan2.2-14B (Wan et al.) 2025) simulate complex dynamic processes with remarkable physical fidelity. Together, these generative priors enable the creation of training data with precise and controllable supervision signals, which are essential for training models to perform fine-grained. physically realistic edits. We find that fine-tuning the baseline on PICA-100K enhances the model's performance in real-world evaluation.
Third, the rapid progress in generative modeling has unlocked new
PICA-100K dataset. As shown Fig. 5 we begin by constructing two structured prompt dictionaries: a Subject Dictionary and a Scene Dictionary, which include wide array of subjects and environments (e.g., "a tea pot," "a black kitchen table"). These entries are paired using handcrafted text-to-image (T21) templates and further refined using GPT-5, resulting in high-quality natural language instructions. The refined instructions are passed to the FLUX. 1-Kea-d (Lee et al. 2025) to generate static source images that are both visually realistic and semantically diverse.
Next, we generate motion-oriented instructions to simulate physical edits. This is accomplished by designing series of 12V instruction templates, describing plausible motion-based changes such as rotations, movements, or tilts. These templates are expanded using GPT-5 to improve clarity and behavioral precision. The motion instructions (e.g., "remove the tea pot," "tilt the vase until it tips over," or "swing the lantern gently in the wind"" are then applied to the corresponding images using Wan2.2-14B. which synthesizes short video clips depicting the intended physical transformations.
For each video, we extract the first and last frames to construct a (source, edited) image pair. These pairs, along with the corresponding instruction, are used form supervision signals. GPT-5 is employed to annotate each pair automatically, labeling the final frame as the preferred output. This pipeline eliminates the need for manual labeling while maintaining high annotation consistency.
Our final dataset contains 105,085 instruction- based editing samples distributed across eight physics categories. The experimental results in Sec. demonstratte that this pipeline can effectively generate high-quality data, significantly enhancing model performance on physics- image editing tasks.
Comparison with related works. PICA-100K is closely related to recent efforts Chang al. (2025) Rotstein et al. (2025) that utilize video priors on image editing tasks. It differs from them in both motivation and methodology. ByteMorph (Chang al. 2025) is primarily designed for non non-rigid image editing, emphasizing visually salient motions such as articulation, deformation, and large pose or viewpoint changes. However, focus on large motions may hurt models' ability to keep non-edited region unchanged. |Rotstein et al. (2025) proposes training free method, which focuses zero-shot feasibility. It directly leverages a video generation model to simulate the editing process. Our work instead targets physical realism, which represents implicit physics principle of real world. Also, data pipeline allows for more controllable generation where the non-edited regions remain stable.
Training paradigm. To demonstrate the effectiveness of PICA-100K, we fine-tune two representative instruction- based image editing backbones: Image-Edit (Wu et al. 2025a). We employ LoRA (Hu et al. 2022) with rank of 256 for fine-tuning.
--ontext-dev (Batifol et al. 2025) and Qwen-
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Table Quantitative comparison on PICABench-Supperfiiaal evalutte by GPT-5 for instruction- based editing models, where Ace 1, Con f. LP, LSE, GST, LST denote Accuracy (%) and Consistency (dB), Light propagation, Light Source Effects, Global State Transition, Local State Transition respectively, and indicates the best and second best score in a category, respectively.
Model
LSE
Reflection
Refraction
Deformation
Causality
GST
Overall
Acc 1
1
Con
1
1 Con't
Acc 1 Con
Acc 1 Con
Acc
Con
Acc
Con
Ace
Nono Ronana
60.29
27.37
50.30 30.30
28. 14
65.94
25.50
$3.05
25.36
59.90
24.81
55.27
96
60.60
13.55
59.88
24.70
59.87
23.47
GPT-mmage11
61.26
16.82
06.04
15.71
62.39
17.20
59.21
17.00
59.66
17.62
52.88
17:21
70.75
10:62
59.04
15.01
61.08
15.48
Soedream 4.0
62:71
25.28
65.50
27:47
65.77
24.00 86
$3.51
26.55
59.17
24.60
$3.45
26.66
65.12
11:17
66.11
28:54
61.91
23.26
Nano Ronana Pro
70.62
23.78
(70.32)
57.02
27.46
664.9)
25.55
58.65
26.44
72:74
70:27
23.30
GPT-Image-115
62.95
23.54
73.15
22.21
68.13
24.56
02.28
23.89
65.55
25.24
58.51
26.16
74.39
11.34
71.52
67.05
21.73
DIMOO
26.68
43.68
22.17
20.76
25.55
23.70
Uniworld-Vi
42.37
18.50
34.50
19.96
46.04
18.59
46.05
17.48
40.10
18.82
39.52
18.11
22.85
17.62
39.50
19.41
37.68
18.48
Bagel
46.97
34.112
39.25
35.53
49,4
33.11
42.54
28.36
44.25
33:12
39.24
33.51
46,80
10.48
49.27
30.53
45.07
28.42
Bagel-Think
49.88
32.44
50.40
29.10
17.05
33.37
43.12
28.87
88
27.59
38.68
32.85 88
15.70
11,66
50.94
27.28
16.15
26.88
OnnilGen2
25.09
48.77
28.34
56,
27.78
39.00
24.84
44.74
29.28
26.93
12.18
46.79
Hidrean-Ei
22.38
46.
22.87
46.15
44.50
36
StepIX-Edit
45.04
30.38
47.44
27.53
53 /6
29 32
34.21
32.37
45.72
29.71
42.90
30.92
55.85
8.75
46.57
20.92
48.23
24.68
Flux.1 Kontext
54.96
27.58
57.41
25.91
57.50
26.92
36.40
26.76
51.83
28.86
38.12
29.09
48.79
12.52
47.61
25.70
48.93
24.57
Flox. Ko"c
$7.38
27.90
58
$8.49
26.
63.07
26.90
36 40
27.01
53.
29.19
41.07
70 54
47.02
14,44
49.27
27.01
50.64
25.23
A Improvement
+2.42
+0.332
+1.05
+0.61
45:57
+0.07
+0.00
40.25
+1.4
17
40.33
42.95
40.15
-1.77
+1.92
41,66
+1,31
+1.71
40.66
Owen Image Edit
62.95
19.87
61.19
23.07
62.90
21.56
55. 25
23.72
48.66
21.69
48.95
22.65 65
67.33
10.19
54,89
20.26
58.29
19:43
Image- Edit+SF
23.24
71.20
56.01
29.73
57.51
23.66
$4.19
62.13
& Improvement
-1.65
+3.37
+7.07
-1.16
+8.30
43.77
+0.75
+6.01
+8.85
+2.17
+5.24
+2,86
-1.79
+0.38
44.77
+5.34
+3.84
+2.48
Table 2: Performance across different prompt specificity levels. Model performance improves with prompt specificity, indicating that more detailed prompts yield higher performance.
LSE
Reflection
Refraction
Deformation
Causality
GST
LST
Overall
Acc 1 Coa 1
Ase
Con
7 Con
Acc
Con
Acc 1 Coa
Ase
Con 1 Ace T Con
Ace 1 Coa 1
Ase
Con
Bagel superiicial
46.97
33:11
39.35
33:53
49.41
33.11
42.54
28.36
48:25
33:12
39:24
33:51
46.80
10.48
49.27
30.53
45.07
28:42
Bageh-inta
55.93
23.78
61.73
18.94
57.50
28.91
47.37
21.08
29 39.33
22.00
44.87
26.09
57.51
8.11
56.13
23.15
54.06
21.14
Bagel-eshich
62.71
15:39
72:34
13:59
62.39
18.74
55.26
16.68
57.70
15:34
59.55
19.98
77.26
8.14
66.90
15.65
18561
15:20
Flux.1 Komnsst-mpefiiil
54.96
27.58
57.41
25.99
$7.50
26.92
36,80
26.76
51.33
28.86
58.12
29.00
48.79
12.52
47.61
25.70
48.93
24.57
Flus. Kontest- inmeent-ittemmeiiaee
58.60
25.61
63.61
21.91
61.21
26.70
33.33
26.81
54.03
26.58
49.21
26.70
53.42
12.81
49.69
25.61
53.77
23.42
Flux.i Komext explict
61.74
25.51
08.19
21.20
63.24
25.89
42.98
24.89
58.44
24.75
62.39
23.54
70.75
10.70
61.75
23.58
03.20
21.54
Owen image Edi superticial
62.95
19.37
61.19
25.07
62.90
21.56
35.26
23.72
48.66
21.49
48.95
22.65
67.35
10.19
54.89
20.26
58.29
19.43
Quen-Imago-Faam Demeg--Flliintemmmlle
62.47
20.37
65.33
20.78
6661
22.49
44.74
25.54
54.77
24.36
50.49
22.36
67.44
10.91
60.91
22.78
6041
2014
Qwen Image Edit explicit
65.62
17.78
77143
1881
61.59
20.45
51,75
21.29
58.11
21.14
66.38
19.90
73.81
9.76
69.85
19.33
68.02
17.96
Both models trained with effective global batch size of 64 and optimized using AdamW for 10,000 optimization steps on 16 NVIDIA A100 GPUs The learning rate is set to 10-5 5 for FLUU.I-Kontext-dee and 10-4 for Qwen-Image-Fddt.
4 EXPERIMENT
4.1 EVALUATION DETAILS
We evaluate 13 closed- and open-source models, covering most recent image-editing and unified vision-language systems. Closed-source systems include GPT--mmaee- [ GPT--mage-
1.5 OpenAl 2025c), Nano Banana, Nano Banana Pro (Google 2025 and Seedream 4.0 (Seedream
et al. 2025) Open-source baselines include FLUX. Kontext [Batifol et al. 2025), StepIX-Edi (Liu
al. 2025). Bagel (Deng et al.) 2025), Bagel-Think (Deng et al. (025). HiDream-E1.1 (Cai et al.
2025). Uni Wo (Lin et al.
2025) OmniGen2 (Wu et al. 20256). Dwwen-Image-i
(Wu et al.
2025a), and DiMOO (Xin et al. 2025). . All input images are resized proportionally to a maximum
resolution of 1024 on the longer side prior to evaluation. To ensure fairness and reproducibility, we run all models using their default settings from official repositories or web APIs. We choose superficial prompts our default setting.
For PICAEval, we first use the provided annotation masks to crop the edited region from the image. The cropped region is then resized proportionally to 1024 on the longer side before being passed to the VQA- evaluator. This ensures standardized input size while preserving relevant physical cues within the editing region. We report results using both the current ofate-of-the-an closed-source model (GPT-5 (OpenAl) 2025a)) and the leading open-source alternative (Qwen2.5- D0wen2.5-VL-728 (Bai et al. 2025) as VL.M evaluator. For consistency evaluation, we compute PSNR over the non-edited
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Table Ablation Results. We construct real-video-based dataset (Mira400K), The model trained on Mira400K underperforms, highlighting the effectiveness of our targeted synthetic data pipeline.
LP
LSE
Reflection
Refraction
Deformation
Causality
GST
LST
Overall
Model
Acc 1 Con 1 Acc Con
Acc 1 Con
Ace
Con 1 Acc 1 Con
Acc 1 Con
Aze
Con
1 Acc
Con
Acc 1 Con
Flux.1 Kontext
27.58
26.76
38.12
29,60
48.79
12.52
(4.66
25.70
48.93
24.57
+PICA100K
57.38
27.90
58.49
26.58
63.07
26.99
36.40
27.01
53.30
29.19
41.07
29.54
47.02
14.44
49.27
27.01
50.64
25.23
+MIRA
52.06
28.80
54.72
29.92
57.34
27.93
37.28
28.05
49.88
29.48
38.68
32.41
44.59
40.17
41.58
31.22
46.96
32.08
regions by masking out the predicted edit area, thereby measuring how well models preserve the original content outside the editing scope. See Appendix A.3 for details.
4.2 BENCHMARK RESULTS
We are still far from physically realistic image editing. Tab. presents comprehensive evaluation of existing methods. While several closed-source models surpass 60 overall accuracy, the perfor- mance remains far from saturated, leaving substantial headroom for physical realistic image editing. Meanwhile, all open-source models still score below 60, indicating persistent gap in the ability of current image editing models to generate physically realistic outputs.
The gap between understanding and physical realism. Among open-source models, unified architectures consistently underperform compared to dedicated image editing models. Although unified MILLMs attempt to integrate visual understanding and generation within a single framework, the presumed advantage of enhanced world understanding does not translate into improved physical realism. This suggests that stronger understanding alone is insufficient, and effectively coupling understanding with generation remains an open challenge. Tab. 2 presents performance across different prompt specificity levels. As shown in Tab. 2 model accuracy improves as prompts become more detailed. The decrease of consistency can be attributed to the trade-off between improving physical realism and preserving non-edited image regions. However, the gain from intermediate prompts is smaller than that from explicit prompts. We speculate this is due to the lack of internalized physics principles, which prevents models from leveraging the additional information. Interestingly, the Bagel model outperforms Flux Kontext under explicit prompts, likely because its unified architecture enhances long-text comprehension.
Video data helps physics learning. Fine-tuning on our PICA-100K dataset yields consistent improvements across multiple dimensions of physical realism. As shown in Appendix [A.5] our model consistently produces more physically plausible results, while other models often exhibit unrealistic lighting effects, implausible object deformations, invalid state changes. Quantitative results in Tab. 3 further support this. For Flux. 1-Kontex,, our fine-tuned model achieves a +1.7 % improvement in overall accuracy over the base model. In addition, it demonstrates better overall physical consistency, improving from 24.57dB to 25.23dB. Notably, we also observe similar gains on Qwen-Image-Fdiie fine-tuning on PICA-100K improves overall accuracy from 58.2999 28%29% to 62. 13%, pushing an open-soure backbone beyond 60. Overall consistency also improves significantly. increasing from 19.43dB to 21.91dB. These findings suggest that synthetic supervision signals derived from videos can effectively enhance model's capacity for physics-aware image editing. They also validate the effectiveness of our video-to-image data generation pipeline in capturing diverse and complex physical phenomena. However, observe slight drop in global state transition accuracy and causality consistency, possibly due to limitations in directly using first and last frames of a video to represent meaningful state changes. We plan explore more fine-grained strategies extract temporal context and leverage intermediate frames.
We also experimented with using real video data construct an image editing dataset. Following the data pipeline of UniReal (Chen et al., (025),, we employed Miradata (Ju et al., (022)) to generate 400K edited images (MIR (M0R) and trained the model under the same settings. However, as shown in Tab. the model trained on MIRA400K performed even worse in overall accuracy. This further demonstrates the efficiency and effectiveness of our proposed data generation pipeline.
4.3 VALIDITY OF PICAEVAL
We conduct human study using Elo ranking to further validate the effectiveness of PICAEval. As shown in Fig. 6 PICAEval achieves higher correlation with human judgments than the baseline. This result demonstrates that our per-case, region-level human annotations and carefully designed

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Published as a conference paper at ICLR 2026
Pearion Correlation Coefficint mo.95
Pecrsce Correlation Coefficient mo0.22
Pearson Correlatice Coefficient mi..5
PICAEval 5PT5
PIGAEval- PIGAEval-Qwenn Denn-nnnnnnn
Baseline- 0.40
8:40
6.5
Figure 6: Alignment between evaluation results and human preference. We make Pearson correlation analysis between Elo scores from human study and different settings. PICAEval-GPTS, PICAEval- -wen use GPT-5 and Qwen2.5-VL-722B adopts Qwen2.5-VI-72B but without edit region annotations. Results show that incorporating stronger VLMs and region-level infomation yields higher alignment with human preference.
the evaluator respectively. Baseline- Baseline-Qwwn
questions effectively mitigate VLM hallucinations, leading to outcomes that better reflect human preferences. Additional details of the human study are provided Appendix A.4
LIMITATIONS AND FUTURE DIRECTIONS
While our approach demonstrates clear benefits physics-aware image editing, has several limitations. First, the PICA-100K dataset, though effective, is built using relatively simple generation pipeline and remains limited in scale. Second, our model trained purely via supervised finetuning (SFT), which brings modest gains but may underexploit the full potential of data. Third, the current framework only supports single-image inputs, lacking the ability to incorporate multi-image or multi-condition contexts. In future work, aim enhance the data pipeline, explore RL-based post-training, and extend the model to support more expressive conditioning formats.
CONCLUSION
We present PICABench, a new benchmark for evaluating physical realism in image editing, along with PICAEval, region-grounded, QA-based metric for fine-grained assessment. Our results show that current models, still far from producing physically realistic edits. To improve this, we introduce PICA-100K, synthetic dataset derived from videos. Fine-tuning on this dataset significantly boosts physical consistency, demonstrating the promise of video-based supervision. We hope our benchmark, metric, and dataset can drive progress toward physics-awes aware image editing.
ACKNOWLEDGEMENT
This work is supported by Shanghai Artificial Intelligence Laboratory.
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A MORE DETAILS OF PICABENCH
A.1 TASK DEFINITION
A.1.1 OPTICS
Light propagation requires shadows that are geometrically consistent with the dominant light source, including direction, length, softness, and occlusion. Typical failure modes include misaligned or missing cast shadows and flat shading that ignores occluders.
Reflection consistency demands nee-deeendentt behavior for specular highlights and mirror reflec- tions. Mirror images must preserve pose and depth; highlight positions should vary with surface curvature and viewpoint. Failures include "floating" reflections or highlights that remain fixed despite evident shape or view changes.
Refraction requires continuous, coherent background distortion through transparent or translucent media. When edited objects involve glass or water, background edges should bend and scale according interface geometry. with preserved edge continuity. Discontinuous refractive boundaries or inverted distortions indicate violations.
Light-source effects evaluate whether new light-introducing edits (like "add lamp") are consistent with the global illumination context-com casts, shadow penumbra, and brightness falloff should integrate naturally with the scene. Common issues include mismatched color temperatures, overly hard shadows, or inconsistent falloff relative to distance.
A.1.2 MECHANICS
Deformation assesses whether shape changes respect expected material properties. Rigid objects should not bend plastically; elastic deformations should be smooth and bounded. Texture and patterning should warp consistently with geometry rather than tear or duplicate. For instance, changing a chair's height should not collapse its frame or produce rubber-like bending.
Causality requires physically plausible contacts and supports under gravity. Edited objects should not float, interpenetrate, or rest in unstable equilibria (e.g., a heavy object balanced on a non-supporting point). Support relations must imply load transfer and stability. Violations include hovering objects, impossible stacking, and intersecting geometries that break solidity.
A.1.3 STATE TRANSITION
Global transitions affect the entire scene (e.g., day-to-night, dry-to-wet, solid-to-molten) Changes must propagate consistently: illumination color and intensity should update across surfaces; wetness should alter reflectance and darkening on all relevant materials; phase changes should be coherent and, when implied, justified by scene-level cues (e.g., pervasive heat source). Inconsistencies include night skies with daylight shadows or partial melting without corresponding global evidence.
Local transitions involve spatially confined edits (e.g., adding steam, charring an edge, melting corner). These effects must integrate with nearby context and causal cues. Steam implies heat and moisture and may induce local condensation; flames produce light spill and secondary reflections; partial melting should respect material boundaries and continuity. When localized changes ignore surrounding context violate material behavior, the edit becomes physically implausible.
A.2 MORE BENCHMARK RESULTS
Tab. lists the performance of models on PICABench, evaluated by Qwen2.5-VL-722 (Bai et al. 2025). It can be seen that the general rule and conclusion are similar to those suggested by Tab. | Most models have very low scores (below 60), indicating fatal gap in the ability to generate physics-aware images.
We present benchmark results on different prompt levels. The results of PICABench with intermediate prompts evaluated by GPT-5 are shown in Table while the results with explicit prompts evaluated by GPT-5 are provided in T The results of PICABench using intermediate prompts evaluated by
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Table 4: Quantitative comparison on PlCABencc--Spperficaal evaluated by Qwen2.5-VL-72B for instruction- based editing models, where Acc t. Con t. LP, LSE, GST, LST denote Accuracy (%) and Consistency (dB), Light propagation, Light Source Effects, Global State Transition, Local State Transition respectively. and indicates the best and second best score in category, respectively.
Model
LSE
Reflection
Refraction
Deformation
Causality
GST
Overall
Acc
Acc 1
1
1 Con't
Acc 1 Con
Acc 1 Con
Acc
Con
Acc
Con
Acc
Nano Ronana
48.91
27.37
47.98
28. 14
51.43
25.55
$2.63
25.36
44,25
24.81
44, 30
25.00 16.96
52.21
13.55
50.73
24.70
49.08
23.47
GPT-mmage11
58.35
16.82
56.06
15.71
50.76
17.20
63.00
17.00
40.83
17.62
46.27
17:21
63.36
10:62
52.81
15.01
53.96
15.48
Seedream 4.0
55.45
25.28
57.41
27:47
55.82
24
54
26.55
45.77
24.60
47
26.66
59.16
11:17
56.13
28:54
54.23
23.26
Nano Ronana Pro
52.54
57.95
$4.39
27.46
47.19
25.55
50.35
26.44
23.30
GPI-Image-115
54,96
23.54
63
22.21
54.30
24.56
23.89
44.99
25.24
48.95
26.16
66.11
11.34
60.29
56.60
21.73
DIMO
26.68
46,05
20.76
22.56
28.57
23.70
Uniworld-Vi
33.41
18.50
29:11
19.96
32,
18.59
46.49
17.48
26.16
18.82
32.91
18.11
18.54
17.62
29.94
19.41
29.18
18.48
Bagel
38.50
34.12
36.29
35.53
37.27
3
51.75
28.36
30.32
33:12
36.29
33.51
40.18
10.48
40.12
30.53
38.23
28.42
Bagel-Think
39.71
32:44
43.67
29.10
36, 26
33.37
45.68
12.87
33.74
27.59
36.71
32.88
39.62
11,66
39.09
27.28
38.86
26.88
OnnilGen2
25.09
41.78
28.34
45.
27.78
45.25
24.84
32.52
29.28
26.93
42.72
12.18
39.52
Hidrean-Ei
22.38
22.87
20.44
25
StepIX-Edit
37.29
30.38
43.95
43.94
27.53
40.6 64
29 32
43
32.37
32.03
29.71
35.72
30.92
48.79
8.75
38,46
20.92
40.39
24.68
Flux.1 Kontext
48.43
27.58
53.64
25.97
43.84
26.92
43.86
26.76
33.74
28.86
34.04
29.09
41.06
12.52
37.01
25.70
41.07
24.57
Flox. Ko"c
49.64
27.90
51.21
26.58
47.22
26.90
46 49
27.01
33.99
29.19
35.44
70 54
39.29
14,44
40.75
27.01
41.91
25.23
A Improvement
41.21
40.332
-2.43
10.61
43. 38
40.07
+2.63
+0.25
+0.25
40.33
+1 40
40.15
-1.77
+1.92
43.74
+1,31
+0.86
40.66
Owen Image Edit
52.54
19.87
52.02
23.07
89.07
21.
57. 16
13.72
38.14
21.69
42.62
65
57.73
10.19
47,82
20.26
19.71
19:43
Image Edit+SF
52.89
23.24
21.91
55.19
29.73
46.24
21.91
& Improvement
+0.35
+3.37
+8.45
-1.15
+6.1
43.77
-.34
+6.01
+2.85
+2.17
+3.62
+2,86
-2.48
+0.38
43.45
+5.34
+2.35
+2.48
Table 5: Quantitative comparison on PICABench-Intermedin evalutte by GPT-5 for instruction- based editing models, where Acc t. Con t. LP, LSE, GST, LST denote Accuracy (%) and Consistency (dB), Light propagation, Light Source Effects, Global State Transition, Local State Transition respectively. and indicates the best and second best score in category, respectively.
Model
LP
LSE
Rellection
Refraction
Deformation
Causality
GST
LST
Overall
Ace 1
Con f Acc 1
Con
Acc 1 Con
Aze 1 Con
Acc 1 Con 1 Acc f Coa f Ace
Coa
Acc
Con
Ace 1 Con"
Nano Banana
56.17
27.27
63.88
26.79
61.72
25.91
$3.07
26.24
63.57
23.62
63,57
23.77
66.11
13.16
64.86
25.09
62.72
22.91
Secdream 4.0
63.60 38
21.08
70.89
18.35
68.63
22.30
50.83
24.76
63.57
22.17
63.15
21.43
71.30
10.97
63.20
26.78
86
20.06
GPT-lmage-1
66,10
16.58
74.39
14.92
64.08
16.83
62.92
64.30
16.73
63.57
16.37
778.58
10.36
09:23
16.58
68.87
15.22
Banuna
26.60
21,91
70.99
26.04
24.09
76.82
11.19
GPT--mage-15
67.31
21.99
16.93
69
24.46
61 84
22.75
70.79
23.82
67.93
23.31
79.25
10.66
72.35
24.41
32
20.37
D:MOO
16.25
23.86
31
26.85
43.00
22.55
32.00
23.38
40.34
25.
34,32
23.09
21.30
23.75
39.09
25.55
34.78
21.09
Uniworid-Vi
47.22
18:71
39:35
19.44
49.75
18.75
32
19.52
45.97
19.39
36.99
18.39
26.27
17.35
39.71
19.24
38.09
19.62
Bagel
55.93
23:78
61.73
18.94
$7.50
28.91
47.37
21.08
49.58
22.60
44,87
26.09
57,51
8.81
56.13
23.15
54.06
21.14
Bagel- Think
18.91
28:45
55.53
21.74
50.93
31.55
50.58
26.15
53. 53.30
26.02
42.76
30.18
53.53
9.88
52.39
25.64
50.71
21.04
Onnaiinn2
$7.38
17.15
22
17.63
42.96
52.32
20.64
21.41
48.79
12:74
16.78
Hidream-Fi.1
56.17
17.01
62 26
17.89
17.43
43.36
18.44
52.08
16.95
44,16
18. 18.34
61.92
9.23
50.04
16.16
54.45
15.82
StepiX-Bdit
52.06
27.83
56.06
23.59
28.79
29.83
53.06
26.95
44.02
25.61
60.26
10.24
52.39
23.85
23.36
Flux. Kontext
58.60
25.61
63
23.91
61
26.70
33
26.81
54.03
26
49.2
26 70
53.42
12.81
49.69
25.61
53.77
23:42
Flox. SS
61.26
27.23
65
25.73
63.43
26.73
30.47
76.54
53.92
27
48 101
27
52.
76
14.38
51.22
27:16
59
24:53
& Improvement
+2.66
+1.62
-0.54
+1.82
+1,22
+0.03
+6.14
-027
+4.99
+0.83
1.13
+1.11
0.66
+1.57
+3.53
+1.55
+1.82
+1.11
Qwan-Image-FF Fdit
62.47
20.37
65
23
20.28
66.61
22.49
44.74
25.54
54.77
74.26
50.49
22.36
67.44
10.91
60.91
22.78
60.1
20.14
Quas-Image-EditssS
63.40
21.70
69.67
18.96
24.43
56,81
23.66
10.93
26.20
63.25
Improwement
+1.33
-1.32
+3.77
+10.20
+6.32
+1.30
-1.73
40.02
Qwen2.5-VL-72B is shown in Table 7 and the results of PICABench with explicit prompts evaluated by GPT-5 are reported in Table 8
As demonstrated above, model performance improves with more informative prompts. Furthermore, the fine-tuned model consistently outperforms the baseline, indicating the effectiveness of the PICA 100K dataset.
A.3 DETAILS OF BENCHMARK METRICS
We provide detailed definition of accuracy and consistency as follows. Let N be the total number of annotated QA pairs, à; be the VI.M-predicted answer for the i-th question, a, be the reference answer, and II(-) the indicator function. The accuracy defined as:
N
Acc
M 1=1
I(a. = a4)
(1)
N
15

---

Published as a conference paper at ICLR 2026
Table 6: Quantitative comparison on PICABench-Explicid evaluated by GPT-5 for instruction- based editing models, where Ace 1. Con t. LP, LSE, GST, LST denote Accuracy (%) and Consistency (dB), Light propagation, Light Source Effects, Global State Transition, Local State Transition respectively, and indicates the best and second best score in a category, respectively.
Model
L.
LSE
Reflection
Refraction
Deformation
Cansality
GST
LST
Overall
Acc 1
Con
Acc 1
Con
Acc
Con
Acc
Con
Acc I Con
Acc 1 Con
Ace
Con
Acc
Con
Ace
Con
Nano Banana
63.92
26.98
66 23
25.89
68.30
25.12
54.39
25.79
66.99
24.41
68.21
34.87
69.32
15.37
64.66
25.50
66.46
23.02
GPT-Image-1
63.92
72.51
14.97
66.44
16.46
60.53
16.73
63.08
16.87
63.85
16.40
78.48
10.46
64.66
16,44
68.07
Nano Banuna
65.13
26.75
74.12
22.26
65.80
62.28
71.84
74.82
$1.46
11.74
26.12
22.15
Stedream
19.87
16.76
69.14
20.42
60.53
69.93
76.23
20.58
92.23
9.09
76.72
24.95
18.73
Gma--mage-1.5
07.31
21.25
83.56
16.38
09.31
23.25
665.2
22:33
73:26
22
77.07
22.94
84.66
10.55
78.17
25.01
76.06
19.62
D&MOO
44.55
24.72
29.92
28.06
20.47
23.17
26.52
25.56
47.45
25
53.19
24.35
20.86
28.74
41.38
25.66
34.39
24.65
Uniworld-Vi
47.70
17:95
42.86
19.03
51.77
18.34
31.58
19.05
49.33 39
18.76
38.82
18.17
35.76
15.78
46.15
18.33
42.78
17.96
Ragel
62.71
15.39
72.
24.34
13.89
62.39
18.74
55.26
16.68
57.70
15 24
59.
19.98
77.26
8.14
90
15.65
65.61
15.20
Thiel-Think
52.54
22.005
60
17.96
24.63
24.64
48,
9.34
22.34
OunaGenn2
57.14
12.81
14.62
64.59
17.54
46.93
18.02
56.72
15.91
55.98
59,82
10.39
51.98
15.38
15.19
Hidream-E1.1
5787
14.76
15.46
61.21
13332
46,29
15.91
64.06
15.08
56.70
16.00
11.30
9.35
00.71
15.52
14.40
Xtep1X-Edi
53:45
22.26
62.
26
18.86
61.55
25.38
40.79
76.99
58.92
25.
56
26
67. 44
9.42
58.6 63
23.52
59.73
21.25
Flux.1 Kontext
61.74
25.61
08.
(8.19
21.20
63.24
25.89
42.98
24.89
58.44
24.75
62.59
23.54
70.75
10.70
61.75
23.68
63.30
21.54
Flux.1 FF
62.95
26.82
69.00
23.04
63.91
26.07
46.05
25.68
63.81
26.47
66.10
25.15
67.99
11.08
63.20
25.19
64.47
22.63
à Improvement
+1.21
+1.21
+0.81
+1.84
+0.67
+0.18
+3.07
+0.79
+5.37
+1.72
+3.51
+1.61
2.776
+0.38
+1.45
+1.51
+1.09
Emage-Fiit
65.62
17.78
71
18.81
64.50
25
58.44
21
66
9.76
19.33
Owen-Image Image Edi
20.16
16.90
20.27
76.16
& Improwement
-1.55
+2.38
+6.43
-1.91
+4.98
96
-1.02
+0.59
+10.88
-0.89
+3.51
+0.37
-2.65
+0.27
4042
+5.11
+2,03
+0.92
Table 7: Quantitative comparison on for instruction-basee editing models, where Acc 1. Con f. LP, LSE, GST, LST denote Accuracy (%) and Consisteney (dB), Light propagation, Light Source Effects, Global State Transition, Local State Transition respectively. and indicates the best and second best score category, respectively.
evaluated by Qwen2.5-VL-72B
Model
LP
LSE
Rellection
Refraction
Deformation
Causality
GST
LST
Overall
Acc
Coe
Ace
Con
Acc 1 Con Ace 1 Con t Acc
Coa
Acc
Con
Acc
Con
Acc
Con
Ace
Con
Nano Ronana
27.27
50.67
26.79
50.93
25.91
49.12
26.24
43.77
23,62
$1 34
23.77
56,84
13.16
53.22
25.09
$1.51
22,91
GPT-Image-1
16.85
16.73
Stedream 4.0
57.14
21.08
63
18:35
56
22.
55.70
24.76
51.10
22.17
55.56
21
66.00
10:97
55.00
78
58.24
20:06
Nono Ranann Pro
58.60
26,60
60.65
21.91
56.66
26
9906
26.53
49.88
26.04
557 3K
24.00
67.88
11.19
15046
26.65
59.21
22.55
GPT-Image-11
58.60
21.99
06.30
16.93
56.49
24.46
57.02
22.75
51..2
25.82
58.37
23.31
71.000
10.66
60:91
24.41
60.63
20.37
D6MO0
37.33
23.86
23. 18
26.85
91.87
22.55
38.00
23.38
26.21
25.12
$1.08
23.09
17.66
25.75
29.52
25.55
27
34.09
Uniworld-Vi
18:71
31.81
33.22
18.75
31.14
19.52
29.58
19.39
38
18.39
19.65
17.33
33.68
19:24
Bagel
23.78
25:91
56.14
21.08
37.41
22.60
36.85
21.14
Rigel-Think
42.37
28:45
47.71
21.74
53.05
24.11
39.12
26,02
37.55
45.70
9.88
74.04
OmniGen2
47.70
17.15
49.60
17.63
46.
20
44.74
21.34
35.70
20.64
58.12
21
39.40
12.74
31.39
16.78
40.90
18.19
HidreansE1.1
46.73
17.01
$3.
17.80
46
17
48.05 68
18.44
38.39
16.95
38.20
18.33
$6.40
9.23
41.16
16.16
46.52
15.8
MepiiXEEdir
42.13
27.83
50.13
23.59
43.17
28.79
46.05
29.83
36.43
26.95
39.52
28 61
52.76
10.24
43.66
23,85
44.72
23.76
Flux.1 Kontett
48.91
25.61
55.53
23.91
45.87
26
43.56
26.81
38.14
26.5
30
26 70
44.15
12.81
43.87
25.61
45.28
23:42
Kot
27.23
58.
A Improvement
-1.21
+1.62
42.69
42.87
40.03
43.63
20.27
43.42
40.83
-1.97
+1.11
-1.58
41.57
40.62
+1.55
+0.34
+1.11
Qwen Image Edit
56.24
20.37
58.49
20.28
50.42
22.49
49.12
25.54
42.30
24.26
43.46
22.36
57.40
10.91
50.31
22.78
50.97
20.14
Owen Image FT
56.59
21.70
58.89
18.96
$1.11
21.92
53.73
28.74
43.62
24.43
50.01
23.66
51,93
10.93
51.20
26.70
$2.88
21.31
& Improvement
+2.35
+1.33
+0.40
-1.32
43.99
+2.43
64.61
43.20
+1.32
+0.17
46.55
+1.0
-247
40.02
+0.89
+3.92
+1.1
+1.17
We use psnr of non-edited region as consistency. For each image pair, we compute the PSNR over non-edited pixels, using binary mask M,(p) where M,(p) 1 denotes an edited pixel and (p) = otherwise. Define the non-edited region as Ω2 = {p Mp(p) = 0}, where p indexes
pixels. Let 1 be the source image and redlt the edited image.
The MSE (mean squared error) over the non-edited region is:
MSE,
1
|ppditt (p) - (p)l
(2)
2
PEO,
Then, the per-sample consistency score (PSNR) is:
2AX²
Con+ = 10 logio
(3)
MSE,
16

---

Published as a conference paper at ICLR 2026
Table 8: Quantitative comparison on Expp evaluated by Qwen2.5-VL-72B for iestruction-based editing models, where Acc 1. Con t. LP, LSE, GST, LST denote Accuracy (%)
and Consistency (dB), Light propagation, Light Source Effects, Global State Transition, Local State Transition respectively. and indicates the best and second best score in category, respectively.
Reflection
Refraction
Causalitty
1
1
T Con
1
T Con
Con
1
Ace
Nono Ronana
53.27
26.98
54.45
25.89
55.99
25.12
56.58
25.79
47.68
24.41
58.93
24.77
58.17
13.37
52.81
25.50
55.40
23.02
GPI-Image-1
59.56
16.34
61.99
14.97
52.61
16,46
61.84
16.73
44.99
16.87
53.16
16.40
70.53
10:46
51.56
16.44
57.83
15.16
Nano Bonana Pro
59.32
26.75
64.60
22.26
66.38
26.27
60.09
27.12
53.55
24.9K
64.70
25.08
72.08
11.74
63.41
26.12
63.29
22.75
Scedream
58.84
20.42
62.72
24.77
GPT-Image-11
61.21
23.25
22.33
57.18
22.25
Uniworld-Vi
37.77
17.95
34.50
19.03
37.44
18.34
30.20
19.06
30.32
18.76
34.15
18.17
28.81
15.78
38.67
18.93
33.80
17.96
Bagel
54,48
15.39
63
13.89
52.28
18:74
55.90
16.68
42.05
15.24
52.32
19.98
68.43
8.14
54.05
15.65
56.44
15.20
Bagel-Think
42.86
27.05
52.29
17.96
13.17
29.35
48.25
21,63
40.10
24.64
38.40
28.01
53.86
9.34
23.38
15.91
22.34
Onnilien2
$1.09
12.81
47.98
48, 78.74
18.02
42.79
Hidream-
StepIX-Edit
43.10
22.26
52.
18.86
47.0
25. 58
47.3
26.99
40.34
25.12
46.69
26.01
57.95
9.42
47.19
23.50
21.25
Flux.1 Kontext
52.54
25.61
59.57
21.20
51.10
25
46.05
24.89
40.59
24.75
53.16
62.03
10.70
54.47
23.68
53.84
21.54
Flox KontsttS/"
$2.06
26.82
59 10
23.04
52.78
26
47.37
25.68
42.79
26.47
53.75
25.15
61.48
11.08
53.64
25.19
54.18
22.63
A Improvement
-0.48
+1.21
-0.27
11 84
41. 68
40.18
1.332
40.79
42.20
+1.72
40.57
+1.61
-0.55
40.38
-0.81
+1.51
10.34
+1.09
Owen Image Edit
$4,00
17.78
60.38
18.81
52.26
20.45
53.07
24.29
44.74
21.14
56.68
19.90
65.78
9.76
59.04
19.33
57.00
17.96
Image
24.88
& Improvement
+0.69
+2.38
+3.56
-1.91
+4.06
+1.86
+1.54
+0.39
+3.87
-0.89
+0.09
+0.37
-146
+0.27
+1.31
+5.11
+1:31
+0.92
Finally, the dataset-level consistency is computed as the average across all N samples:
Con 1 N
Con:
(4)
N
Here, MAX is the maximum pixel value (c.g., 255 for 8-bit images).
A.4 DETAILED HUMAN EVALUATION PROTOCOL
Study setup. We use the Rapidata platform to conduct pairwise human preference comparisons for evaluating image editing quality. Each trial presents reference image and two model outputs (A/B) under a fixed unified instruction:
Select the image that more closely matches the editing instruction.
The A/B order is randomized per trial. Annotators vetted beforchand and participate on voluntary basis.
Datasets and models. We evaluate 9 models over the PICABench dataset at three difficulty levels (superficial, intermediate, explicit), forming 36 unordered model pairs per item. For each difficulty, sample 50 items via stratified sampling over the phys i cs law taxonomy. Each item yields 36 comparisons, each judged by annotators, resulting in 7,000 votes per split.
Elo computation. To aggregate preferences, model A and B with current ratings (RA, RB), the expected win probability of A is:
robust Elo rating system. For match between
E
(5)
RR RA
+
where S 400 is the scaling factor.
Given the vote ratio E [0, 1] for model A, with 88 - 8A, the ratings are updated as:
R'A = max (Rmin, RA + Koa EA)).
(6)
R'B (axin Rg + Ken (8g Eg)),
where KaF = K adjusts for vote count v = UA + CB. and K = 24 the base step size.
hhtpss/wwww.mppdaaaaaii
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Robust aggregation. To reduce order effects and improve stability, shuffle the comparison stream and re-run Elo updates for T 50 rounds. The final Elo score for model m computed as:
T T
T
Rm
R m
Rm
Rm
Om
T
Parameter setting. Table summarizes the Elo configuration used in all human evaluations.
Input
Chain-of-Thought
Ragel-Think
The user wonts the vallow nemoved
Table 9: Elo parameter setting.
the tneping shadow the should be erssed match the surrourding floor eneuring
Parameter
Value
lighting transitinn
Initial Elo rating
1,000
Elo scaling factor
400
to mantan realis..
Base K-factor
24
Minimum Elo rating Rmin
700
Number of rounds T Votes per match Model pairs per item Items per difficulty
50
atructure and layout of roam, induuing the furmitur,, lighting and decor, ramain unchangad. The odjustment
3
45
50
Furn off
Benchmark splits
3
the risit the image intact.
Total comparisons per split
6,750
Total votes per split
20,250
Figure 7: Examples of Bagel's reasoning trace.
A.5 MORE VISUALIZATION
Fig. 15 presents generated images of various models prompted by samples our PICABench. where "Ours" indicates our fine-tuned K-ontext model. The prompts cover all eight physics laws and three complexity levels. They demonstrate that the performance of these models varies considerably in complying with physical laws. Most models either just perform superficial edits and ignore the physics law, or completely fail understand the instruction. Only a few models, including ours, can yield physically plausible images in most cases. Therefore, the ability to follow physical laws is crucial but lacking in most models, and by PICABench we hope to draw the community's attention to this critical problem.
Moreover, we show Bagel's think process in Fig. 7 As shown in Fig. 7 model successfully reasons the correct results in its chain-of-thought, yet fails execute them in the generated image.
A.6 PROMPTS FOR PICABENCE
We present the prompts used in constructing PICABench. Fig. 17 shows the prompt used generate QAs to evaluate editing models' performance. Fig. 16 shows the prompts used to generate edit instructions PICA-100K.
B MORE DETAILS ABOUT PICA-100K
B. EXAMPLE OF PICK-100K
Fig. 18 shows some examples in PICA-100K dataset. For each pair, we focus on the manifestation of physical laws.
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Superficial Prompt: Remove the yellow chair
Input
Ours
Flux.1 Kontext
BAGEL-Thime
Step X-Edit
COON
GPT-Image
Nano Banana
HiDream-E11
Seedream 4.0
DIMOO
Superficial Prompt: Move the potted plant to left side of the table.
Input
Ours
Flux.1 Kontext
BAGEL-Think
Step
GPT-Image
Nano Banana
Qwen-Image
HiDream-ELl
OmniGen2
Figure 8: Examples of how models follow the law of light propagation in optics (superficial propmts).
B.2 PROMPTS FOR PICA-100K
We show the prompts used in constructing PICA-100K. Fig. shows the prompt used in generating video from generated images. Fig. 20 shows the prompts used to generate edit instructions in PICA-100K
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Published as a conference paper at ICLR 2026
Superficial Prompt: Turn on the lamp on the bedside table.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL-Think
Step1X-Edit
GPT-Image
Nano Banana
HiDream-E1.I
OmniGen2
Superficial Prompt: Turn off the lamp in the room.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL-Think
GPT-Image
Nano Banana
Owen-Image
HiDream-E1.1
OmniGen2
Figure 9: Examples of how models follow the law of light source effects in optics (superficial propmts).
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Published as a conference paper at ICLR 2026
Superficial Prompt: Move the hot air balloon in the image to the left.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL-Thing
GPT-Image
Qwen-Image
HiDream-E1.I
Seedream 4.0
OmniGen2
Superficial Prompt: Move the man to the right.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL- -hinkk
GPT--magee
Qwen-Image
HiDream-E1.1
Seedream 4.0
OmniGen2
Figure 10: Examples of how models follow the law of reflection optics (superficial propmts).
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Superficial Prompt: Remove the magnifying glass from the image.
Input
Ours
Flux.1 Kontext
BAGEL- -AGEL-Think
GPT-Image 1
Qwen-Image
HiDream-E1.I
Seedream 4.0
DÍMO0
OmniGen2
Superficial Prompt: Add blue straw the glass of water.
Input
Ours
Flux.1 Kontext
BAGEL
GPT-Image 1
Nano Banana
Qwen-Image
HiDream-E1.1
Seedream 4.0
OmniGen2
Figure 11: Examples of how models follow the law of refraction in optics (superficial propmts).
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Published as a conference paper at ICLR 2026
Superficial Prompt: Add dog top of the pillow.
Input
Ours
Flux.1 Kontext
BAGEL
GPT-Image 1
Nano Banana
Qwen-Image
HiDream-F111
Seedream 4.0
OmniGen2
Superficial Prompt: Change the woman's position from sitting to standing.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL- Think
StepIX-Edit
Nano Banana
HiDream-EL.1
DIMOO
OmniGen2
Figure 12: Examples of how models follow the law of deformation mechanics (superficial propmts).
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Published as a conference paper at ICLR 2026
Superficial Prompt: Remove the skateboard.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL- Think
StepIX-Edit
GPT-Image
Qwen-Image
DIMOO
OmniGen2
Superficial Prompt: Remove the white table.
829
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL-Thin
GPT-Image
Nano Banana
Qwen-Image
HiDream-F1.1
Seedream 4.0
Figure 13: Examples of how models follow the law of causality in mechanics (superficial propmts).
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Superficial Prompt: Change the foggy weather to sunny day.
Input
Ours
Flux.1 Kontext
BAGEL-Think
Step1X-Edit
GPTT-mmaee
Nano Banana
Qwen-Image
HiDream-E1.1
OmniGen2
Superficial Prompt: Change the weather in the scene to a snowy day.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL Think
X-eptX-Edit
GPT-Image
Qwen-Image
HiDream-E1.1
Seedream 4.0
Figure 14: Examples of how models follow the law of global state transition (superficial propmts).
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Published as a conference paper at ICLR 2026
Superficial Prompt: Make the bench wet.
Input
Ours
Flux.1 Kontext
BAGEL
BAGEL-Thine -hhink
Step1X-Edit
GPT--mmgee
Nano Banana
HiDream-E1.1
OmniGen2
Superficial Prompt: Freeze the soda can.
Input
Ours
Flux.1 Kontext
BAGEL-Think
Step1X-Edit X-Edit
Mundet
GP'T-Image
Qwen-Image
HiDream-E1.1
Seedream 4.0
OmniGen2
Figure 15: Examples how models follow the law of local in state transition (superficial propmts).
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Published as a conference paper at ICLR 2026
Prompt for PICABench Edit Instruction Generation
Your
"intammediat""
"suporficial"
related (operation)
You provided with
"Immag"" wondd ressoning.
shouls "exiaiing instruction": A user providdd righ-llvvll deso desoription ppliioni of the intended editng operation. may be or encoded with physical scientite
textures, lighting, material,
imprication.
A desoriptio of the physical laws scientiic constraints that the editing operation must respect. This ensures the remains physically pluusible and visually coherent.
physics soqurement"
Your goal is intrppee the instruction ""hrre levele undenstanding". each reflecting distinct stage in physicca reasoning and visual modeling "Iimporent"" : The outbut image should reflect the "phyysccally stable result after the edit has taken full effectt not the transstional moment when the edi har octumed For ecomple, i support is removed. lilling or deformation should be shown as wimeedy completed. Aucid depicting unstabble transil onal statess
"Supertcial Prompt":
Reformutate the instruction into Heral surface-levei command that includes ony the brsic editing action.
"triemmdiate Promppt
or physical outcomes" indioatin physical expected, without cling randering consequeenes. prompt bridges super explich introducing real-wordd mechantsm awareness without spezitying appearance.
Reformlltee
adding enese relevsni physical constraints,
Prompt""
Reformstd physics physioally scientfic transformaion"
aligne
interaction gravit-afiven physically
reflecion). instamtancuus effects.
should
describe
instrcctio:: (existing instruction
Hare the physics requirment (phyakts requirmmnn)
Please output the tollowing format:
("superfical al prompe" yyour supertisaal promot". "Ineereddatee prompt: "your intemeeiate prompe".
"erpllt_ "you explict prompt"])
Figure 16: Prompt used to generate edit instruction for PICABench.
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Prompt for QA Generation
You an expert in image ecttngg evaluation. Your task to generate specifi, targeted QA pairs assess the success of this image edting task.
EDITING TASK CONTEXT:
Edil Instrction fodit Instruction)
Physics Law: (physies la))
Operation Type: (operatio))
CRITICAL CONSTRAINT: The eveluator will ONLY see the final edited image and the edit instruttion They CANNOT see the original Imsge for comparison.
Therefore, all questions must be answorable by looking at the edited image alone.
GENERATE QUESTIONS FOR TWO CATEGORIES:
EDITING COMPLETION ABSESSMENT:
Your Verify that the speclic
recuested instruction visble the firel obyect rnferoood the instrctionn locitor within noun plision (for disimbiguston) not additional
Always eupl isplisilly noal oe predicate..
Duild the locator
combining of fsllowing needed uniqusly identify the target: tollett ofright ofinn front ofibhinnioon
httppoootoomemeemeeeee. woweinddeee/rrgrruuuu aegpoundtccggouusuuupeeo oB undeerinddeeeee (eeferncce obyeaj) ontinal (lrtmoetigghmmotee wwwpmm/oeggimmmmmeeeeeeeeee www.oooeeeemoeteiieeee contentirst from the left,, sttributes
quadrant) melatvv positio
icoloripateemmmm
relationshiies (attached tamonginng frompplaced on).
Focus observable charactenisics
PHYSICS CONSISTENCY ASSESEMENT:
whether
laws law).
Check
follows law)
for physically impessible orientatins,
whether stalo.
refections, interactions
Foeus
MANDATORY SINCLE T-RONT
localized "sucept" mutiple chacks needed, them multipie single preentabbent, desmmiiuttioo)
DO are
"or", "both" "alther,
facinglorente bouchingloversppping ceeting
QUESTION
target g.g. (lcatorr phrase] (object] movelpastion on location terget.
new (e.g. "on
window", sofa").
coneettrnimmmterallla ask single predicate (e.g. exactly [localized objendd" ).
target.
condise, concreto lang language. vague "some"" "appoars Tooks like.
Keep
as possible maintaining darlty.
language. You solt complex checks multiole simple questions.
anblyulty, ensure single Prrpppaation..
spwafii usi clear localor phosse to disembiguate inetanoss when the category appears muttiple
Frame quastions pasiivaly.
Cover key aspacts instuctionn multple atomic quastions neaded, question should target a diffanent sepect.
CRITICAL Every answer must be ecectly "Yee" "No" other values are acceptable, not leave any answer empty.
OUTPUT FORMAT:
"Edtiig Completion GA" (1 ("queetion" "answer" "Yoe"J
[oouestion: "nnwwer: "No"l]
Physics Consistercy GA:E
(Peuestion "answrr" "Yoz"),
|Puuestonn "nswwr"" "No"1]
9
EXAMPLES (DO NOT OUTPUT THIS SECTION)
BAD jambiguous targets "S there a tabl??
-000 (leclieed, Absence predicate): "Is thare a mund wonden table the ownter foregrane?"
BAD (lwo predicates): the central tacle removed
is the floor clern"
GOOD (spiltbll, sinlle precicato asample): 'Is there a central round table?" (sepersta any florrvaloted check)
GOOD (addition: "Is there small blue cup the right edge of the desk?"
GOOD (mowe) "l theme traffic. cone placed on the left side of the croeswalk?
GOOD (utribune) the witmostt the two vases red?"
GOOD (phyalcs)) "ts the shadow of the lamp cast toward the lower-ight, cons stent with top-left light source?"
Remember Questioon should evaluate the final image state, not compare with the original or ask about the editing process. MOST IMPORTANTLYY Ensure each queetion evellstten uttes difieeent sepect avoid asking the same thing with different words.
Figure 17: Prompt used generate QAs for PICABench
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update coherently. Published as a conference paper at ICLR 2026
Figure 18: Examples of PICA-100K dataset.
Prompt for 12V Caption Generation
imaga-to-idde
imago. NOT quessions. NOT mantion traggaphaikieediipooo...
Goal
ONE concise
that VISUALLY ORVIOUS change
change" change ge light sourca/addiiemeee shseeseee intensity). adsinenowsimvvlle eplace objjcct lncaligkbbal matarial state.
The secondary: keep camera
necessary visbiiity.
stepa (illntly: intermadiste output)
scene: slck sallent, nameable objects; supporting surface(s), light sources, minors/glassswwteer sotutelormatie materials, stackssspports..
2) Choose ONE change that (lww_fg) plausibly changes).
option (area
sutject global
measurable: include dreedton-magbuuue go.... right by with", "shadow doubles longth" hamp off, dropicts form").
4) Keep and layout stable (lw imples globel charge (onty "Gicbal")
Camera: by default tay "camera stati" using camera, esactyy ONE simple motion keep content change primary.
scenos profer Light, Procaraaton: "adshemsvelmoveieee"
playbock (pick ONE
feasible, high sal option)
Light Source, Effect::
lamp" change color; slighlly move
object
so
postooneegg updates single key light.
Roflection "sddiiorreeemosessspeee an object front of or glossy surface: the "rrgion seen through" shows realisti wwwrping/dippersioon
image" apdiassidispppaars consistarti ity,
Refraction: "addhemovettrrii ovvereelaaae" object transparent medium (glass/wator
Deforaator:: "addiremove weight" apply pressure
(pillo,, foam, fabrio create "ddeop Indertation partial reacund".
Causality: "rrmmve a support
center
collapees under graviy"".
(stata change)
local state change on largel objacts: indude not iniited www.byytuuetioonnsllleeeeee wabeeaaktttttteeeeeereeeeaa
strong
cuss (draplais, gloss, soot, Global (sceno (oceno-wldo) "dme/scason/weddti" shitt" so lighting shadows, materiats, vegetation, and almoaphere update "ccherendy"" across whole sceno.
Hard seconds, single
shot, expossre.
"Primary change must contant change"" not camera-only motion.
new objects unlees your choeen change requires "add"; deletions unlees your chosen change "remove".
Use the most concrete nours visible ecena (e g. "deek lamp'. 'oecemic mug" "minor", "glees of weler", "pllow"; "book stack") mete language, not
Style
ONE sentences expliit camera state the end l'camera static' "slow push-in" truly needed).
inclide at lesest plysics que word when melevant:
shadoowwhigthightt (iiht)) reflctioon wwrpingediipariinn (refraction) indentaaioninbouudd (daforaatonn) Hitsddwloolllpe (ciusality). drapeets/euttoootissees (state), cotor pomppersture/ommoppptt (global).
Ourpot format
Retum ONLY valld JSON:
[1"2v prompt": "cone sentence captone"]]
Good outputs (examples; do NOT copy blindly)
Light Source Effects: "The desk lamp tums and all previously It arozs into dimness while shadows vanish soften across the dosk, camere statio."
Light. Propagaton: "The ceramio mag slices right by abcut half lis dtth and, under left key light its sharp shadow shifts right and shortens, camera state."
Reffectio: 'A red apple appears before the miror and is miror image pops into the correst mirored positio with matching highiights, camera static.
Refroctione "The spoon moves left behind the glass water and the portion seen through the glass warps and shitss with sight color discers on, camera static
Daforation: "A dumbbell placed on the aillow, farming doep indertation with dlaar contact patch and slow partill rabound after setting, camara statis"
Causaliy: by. "The small support block removed from the book stack and the upper books ill and slide cown in continuuus gravity-divven collapse, camera
stado.
Local wat: "Dwnse droplets quiekly form on the fabric and the surface deriars with bright speriier glints while the suroundings ramain uncharged, camera
stade.*
Global time: "Light shifls sunset shadows grow langer and warmer uniformly across all objocts, camera state.
Figure 19: Prompts used generate i2v caption for Wan2.2 14B
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Published as a conference paper at ICLR 2026
Prompt for PICA100K Edit Instruction Generation
annotation.
instructionss)
provided with:
Image" target image adting
"OOuppt
changes input oulput imageo genersie edting instrucion(s) reflecting specified depth uncenstending
traneformation.
image target
images
NOT
image". image" "argel image". mage" made,
For explict prompte: Focus editig image,
changes
changes. implementation details, specfic
impossible operations "generate depth or manifestation, Describe physical principles serms
processes.
"SSuerficial Promotth
prompt obvious change
simple imporatio ony the primary visual change.
that
"Remove... "Change.."" "Make...".
"intenmddate
Keep internediate prompt concise
Genarate edtting action
physccl explenations. bridges effects processes expected. command image.
undertying processes mecharisms,
superficial expliot by indicating
"Explictt Prompa" Keep expicit prompt concisa under
deteled aditig insttutionn
changes undarlying physical principles that cause
What visual changes cocur (onjects, lighting, materials, Instures co compostton)
The physical mecheniems behind these changes light behavior, material propertes, environmentsl effects, forces) these physical processes manfeett ooservable visual effects
Write as clear editng commands that cemonstrate understanding roal world plysicss Avaid technical implementation detall, specific numerical parameters, or impossible operations, Focus on rescribing the ptysical cause and effect eeattonships that produce the visual transformaion.
"Examples of what avaid."
Technical details: 'genorate depth maps". "opply Goussian blur". "multaly by transm ttance map"
Specific numbers: "nnresse expesur by +0 EV", "reduca contrest by 30%
Implementalle methods: "use Scroen/Oveddy model, "apply partile layers"
"Examples of what inlude."
Physici afferts: "heavy anowfal ridunne visibiity through light arattering ing"
Visual maniiaa miassaatinn "dstant objects become progressively fainter and lass dillindd
Cause-and d--fflaa ssmosphrr particlss scator light, creating a hazy, west add sesthed-out appearance"
Please ana the inuu-ouuppt pair and generate the three levels of editing instructions.
Please output the following format:
("superiiiaal promptt: "your superficial prompt. 'Interediatte, prompt": your intermedaaee prompt".
"explidt_rroma""" your expicit prompt"]]
Figure 20: Prompts used to generate edit instruction for PICA100K
30


Reviews:
[{"review_id": "r5zaDnUtEr", "reviewer": "Reviewer_Ytia", "summary": "This manuscript proposes a benchmark to evaluate physical realism in image editing. The authors consider physical effects beyond simple instruction completion. They have collected a physics-aware benchmark, PICABench, to evaluate the performance of existing image editing models. Meanwhile, the authors also utilize a VLM-as-a-judge (PICAEval) to evaluate the edited images. Beyond the benchmark, the authors also propose constructing a physics-aware dataset (PICA-100K) from videos. Experimental results demonstrate that existing models still struggle to achieve physical realism. Furthermore, the proposed dataset is shown to enhance the physical realism of a fine-tuned base model.", "questions": "See the weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This manuscript considers the role of physics in the image editing task, which is an interesting and rarely explored area of research. The proposed benchmark is well-constructed and provides a comprehensive analysis of existing models regarding the physical realism of their edits. In addition, the proposed PICA-100K dataset is a clever approach to learn physics from synthetic videos.", "weaknesses": "1. The primary weakness is that the proposed solution, fine-tuning on PICA-100K, yields very small gains. While this is a positive result and better than the baseline model, the small margin doesn't present PICA-100K as a definitive solution. Moreover, the final fine-tuned model still underperforms other top-tier models (e.g., GPT-Image-1, Seedream 4.0, Qwen-Image-Edit) in the overall benchmark, as shown in Table 1.\n\n2. To better demonstrate the dataset's effectiveness, the authors should try fine-tuning other base models (besides Flux.1 Kontext) and report their performance improvements.\n\n3. Meanwhile, the concept of constructing image editing pairs from synthetic videos is not entirely novel and has been explored in previous works (e.g., Frame2Frame[1], ByteMorph[2]). The reviewer recommends the authors explicitly to describe the differences and contributions of their data generation pipeline compared to these prior works.\n\n[1] Pathways on the Image Manifold: Image Editing via Video Generation\n\n[2] ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TyeaJgQNeJ", "reviewer": "Reviewer_guET", "summary": "This paper focuses on the issue of low physical consistency in instruction-based 2D editing results, and proposes PICABench, a benchmark for physical realism preservation capabilities of image editing models. By analyzing common cases of low realism, the paper assesses the physical consistency in terms of optical, mechanical, and state transition aspects of edited image content. Specifically, this paper proposes the VLM-based evaluation metric PICAEval, combining human annotation mechanisms and QA patterns to evaluate the physical realism of 2D editing results. Furthermore, this paper constructs the synthetic image editing dataset PICA-100K based on video generation modalities to fine-tune and improve the physical preservation capabilities of large-scale 2D editing models.\nThrough the evaluation of existing models, this paper shows that \"most existing large-scale models are still unable to achieve physically realistic 2D editing\" and analyzes the gap between instruction understanding and physical preservation in existing models. Moreover, by fine-tuning models with the proposed dataset PICA-100K, this paper demonstrates the effectiveness of PICA-100K.", "questions": "See weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper is a benchmark paper. It analyzes common physical realism issues in existing 2D editing models, and proposes PICABench and PICAEval to evaluate model capabilities from multiple dimensions. The motivation is clear, and it is important for image editing models.\n\n- The paper explores the capabilities of VLM models, the QA evaluation method of LLM, the human annotation process and the advantages of video for dataset construction and method evaluation, which is novel and effective.\n\n- The paper is well-organized and easy to follow.", "weaknesses": "1.  Physical awareness is important for 2D editing mode, but I still concern about requirements that editing with physical awareness directly. \n- It is more like the issue of prompts/instructions. In addition, the definition of hallucination in this paper should be clearer. The 2D editing model needs to balance between precisely edition according to the prompts, and the certain ability to extrapolate. For example, in Fig.1 bottom right (remove the scooter), it is kind of hallucination if the rider stands on the floor, which also violates our prompt. Maybe, the user only simulates a snapshot that the rider flies. \n- The middle or long instructions in Fig.4 (d) are more reasonable. Tab.2 should include more methods in the main paper. \n- Tab.2 constructs different levels of prompt, but the performance is highly impacted by the long-text comprehension. It feels like the problem has changed to 2D editing with long prompts. Therefore, it would be better to compare with methods based on multiple steps instead of one step with long prompts.\n\n2. There is a lack of detailed explanation of metrics Acc and Con. \n\n3. Line 424 claims that \"model performance improves as prompts become more detailed\". However, Tab.2 shows that the consistency (Con) decreases in most cases. Moreover, Line 425 claims that \"the gain from intermediate prompts is much smaller than that from explicit prompts\". However, it is hard to verify on the basis of tables.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ao9CmsDueP", "reviewer": "Reviewer_ghns", "summary": "This paper introduces PICABench, a benchmark for systematically evaluating the physical realism of instruction-based image editing models. The authors argue that existing models and benchmarks primarily focus on semantic fidelity and instruction completion, neglecting crucial physical effects like correct shadows, reflections, and object interactions, which are key to true realism. To enable reliable evaluation, the paper proposes PICAEval, a region-grounded, VLM-as-a-judge protocol that uses per-case, region-level human annotations and binary Yes/No questions (VQA) aligned with specific physical sub-dimensions. Finally, authors propose the PICA-100K, a large-scale, synthetic training dataset (100k examples) and experiments show that fine-tuning an existing model (FLUX.1 Kontext) on PICA-100K significantly enhances its physical consistency.", "questions": "Please see the weakness", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. PICABench covers eight **fine-grained sub-dimensions** and a variety of common editing operations. And PICAEval addresses the known limitations (hallucination, low sensitivity) of standard VLM-as-a-judge setups by incorporating region-level annotations and targeted Q&A.\n\n2. The paper is very clear and **well-structured**. The core dimensions (Optics, Mechanics, State Transition) are immediately intuitive. Key concepts like the eight sub-dimensions are defined with concrete, checkable criteria\n\n3. It introduces a critical **new evaluation standard** that forces the community to move beyond mere content editing toward physically consistent realism.", "weaknesses": "1. The authors note a slight drop in the State Transition Accuracy for their fine-tuned model and speculate this is due to only using the first and last frames of a video to represent meaningful state changes. This highlights a potential weakness in the PICA-100K data generation pipeline for this specific dimension. The use of only two frames might fail to capture the coherence and physical consistency of the transition process itself, as the model only sees the 'before' and 'after' states. While they mention plans to explore fine-grained strategies, the current limitation is a weakness in the proposed training data solution.\n\n2. The metrics used (Chamfer Distance, F-Score) are standard but insufficient to evaluate perceptual quality or shape plausibility. No perceptual or task-level metric is proposed\n\n3. The paper notes that unified multi-modal models consistently underperform compared to dedicated image editing models, speculating that their enhanced world understanding doesn't translate to physical realism due to a lack of \"internalized physics principles\". This is a crucial observation, but the analysis remains largely speculative. A more in-depth qualitative analysis, perhaps showing how a unified model (like Bagel or OmniGen2) fails to integrate its supposed world knowledge into the generation process (e.g., how its explicit reasoning about a scene's physics is decoupled from its final generative output), would strengthen this claim\n\n4. Table 2 shows that performance improves with the specificity of the prompt, with a notably small gain between \"superficial\" and \"intermediate\" prompts compared to \"explicit\" prompts. While the authors speculate this is due to a lack of internalized physics, it could also indicate that the models are highly sensitive to explicit guidance (i.e., the \"explicit\" prompts describe the desired physical outcome) rather than being able to infer the physics from the instruction and scene context (which the \"intermediate\" prompts attempted to provide). This suggests a potential weakness in the model's ability to generalize physical principles from scene context alone", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9tJx7Yl30U", "reviewer": "Reviewer_QMZg", "summary": "This paper addresses a critical gap in image editing by introducing PICABench, a benchmark designed to evaluate the physical realism of edited images, moving beyond mere semantic fidelity. The authors argue that while modern models can follow complex editing instructions, they largely fail to simulate accompanying physical effects (e.g., shadow/reflection updates, mechanical interactions), which is crucial for realism. To systematically assess this, PICABench categorizes physical consistency into eight fine-grained sub-dimensions spanning Optics, Mechanics, and State Transitions, covering most common editing operations. The paper also proposes PICAEval, a reliable evaluation protocol that leverages a VLM-as-a-judge with per-case, region-specific questions grounded by human annotations, thereby reducing hallucinations and improving sensitivity to nuanced physical violations. Beyond benchmarking, the authors explore a solution by learning physics from videos, constructing the PICA-100K dataset. Their experiments show that fine-tuning on PICA-100K significantly enhances a model's physical consistency. The comprehensive evaluation of 11 models reveals that physical realism remains a significant challenge, establishing this work as a foundational step towards physically plausible image editing.", "questions": "See the \"Weaknesses\" section. Additionally, as I have reviewed other higher-quality papers on world knowledge editing and find the choice of the fine-tuned model in this paper somewhat inappropriate, my current score is relatively low. I will adjust my rating based on the rebuttal and other reviewers' comments.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The research on complex instruction-based image editing is highly meaningful, and the proposed method demonstrates improvements over current state-of-the-art approaches.\n- The paper presents substantial work, including the introduction of a new benchmark, the provision of corresponding training data, and fine-tuning of the FLUX model.\n- The writing is clear, and the experimental comparisons and figure illustrations are professional.", "weaknesses": "- The PICA-100K construction pipeline relies on video generation models, meaning the final benchmark performance heavily depends on the quality of WAN. I am concerned about whether such data can ensure sufficient effectiveness, even if it performs well on the proposed benchmark. Given the proliferation of benchmarks for world-knowledge image editing, I question whether this data would generalize well to other benchmarks.\n- The authors fine-tuned the FLUX image generation model and achieved promising results. However, I believe that tasks involving world-knowledge image editing should be improved based on VLM+Diffusion model frameworks, such as Bagel, UniWorld, or Qwen-Image-Edit. While the proposed method outperforms these unified generation-understanding models on PICABench, it may be attributed to the PICA-100K dataset being tailored specifically for this benchmark, allowing a pure diffusion model to overfit to it. It may be unreasonable to request extensive additional experiments during the rebuttal, so I encourage the authors to focus on analyzing this point. My final score will depend on the analysis provided.\n- There has been a surge of related work on world-knowledge editing in recent years. While citing the latest work is not mandatory, some early relevant studies should be referenced:\n\n[1] Echo-4o: Harnessing the power of gpt-4o synthetic images for improved image generation \\\n[2] Anyedit: Mastering unified high-quality image editing for any idea \\\n[3] Editworld: Simulating world dynamics for instruction-following image editing \\\n[4] Reasonpix2pix: instruction reasoning dataset for advanced image editing \\\n[5] Smartedit: Exploring complex instruction-based image editing with multimodal large language models", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)