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Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual Editing
Xiangyu Zhao, Peiyuan Zhang, Kexian Tang, Xiaorong Zhu, Hao Li, Wenhao Chai, Zicheng Zhang, Renqiu Xia, Guangtao Zhai, Junchi Yan, Hua Yang, Xue Yang, Haodong Duan
TL;DR
General visual editing models remain limited in following complex instructions, preserving appearance consistency, and supporting flexible input formats. RISEBench benchmarks reasoning-informed editing across four reasoning categories with multidimensional human and LMM-based evaluation, finding that current models still struggle substantially, including GPT-4o-Image’s low overall accuracy and pronounced weakness on logical tasks.
Problem
General visual editing models have limited ability to follow complex instructions, preserve original appearance, and accommodate flexible input formats.
Method
RISEBench evaluates reasoning-informed visual editing across temporal, causal, spatial, and logical categories using curated test cases and human- and LMM-based assessment.
Results
Current models face significant challenges across reasoning-based editing tasks; GPT-4o-Image performs best overall but remains particularly weak on logical reasoning.
Takeaways & Limitations
RISEBench establishes a structured basis for assessing reasoning-informed visual editing and highlights logical reasoning as a key area for future research and model development.
Takeaways & Limitations
The benchmark’s task categories may not be exhaustive, and its dataset contains only 360 questions.
Abstract
from arXiv · showhide
Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but they still face challenges in General Visual Editing, particularly in following complex instructions, preserving appearance consistency, and supporting flexible input formats. To study this gap, we introduce RISEBench, the first benchmark for evaluating Reasoning-Informed viSual Editing (RISE). RISEBench focuses on four key reasoning categories: Temporal, Causal, Spatial, and Logical Reasoning. We curate high-quality test cases for each category and propose an robust evaluation framework that assesses Instruction Reasoning, Appearance Consistency, and Visual Plausibility with both human judges and the LMM-as-a-judge approach. We conducted experiments evaluating nine prominent visual editing models, comprising both open-source and proprietary models. The evaluation results demonstrate that current models face significant challenges in reasoning-based editing tasks. Even the most powerful model evaluated, GPT-4o-Image, achieves an accuracy of merely 28.8%. RISEBench effectively highlights the limitations of contemporary editing models, provides valuable insights, and indicates potential future directions for the field of reasoning-aware visual editing. Our code and data have been released at https://github.com/PhoenixZ810/RISEBench.
1 Introduction
RISEBench is introduced to evaluate reasoning-informed visual editing, targeting limitations in complex instruction following, appearance preservation, and flexible inputs. It combines four reasoning categories with multidimensional human and LMM-based evaluation and reveals substantial weaknesses in current models.
- RISEBench is presented as a focused benchmark for reasoning-informed visual editing, addressing limitations in complex instruction following, appearance preservation, and flexible input formats.
- The benchmark covers temporal, causal, spatial, and logical reasoning through manually curated, human-annotated test cases.The dataset contains 85 temporal, 90 causal, 100 spatial, and 85 logical samples, totaling 360.
- RISEBench evaluates instruction reasoning, appearance consistency, and generation plausibility using human judges and an LMM-as-a-judge framework.The authors also measure correlations between LMM and human scores to assess the framework’s reliability and effectiveness.
- Open-source editing models show limited reasoning capabilities, while proprietary models achieve substantially better overall performance.GPT-4o-Image performs strongly on temporal, causal, and spatial tasks but remains challenged by logical reasoning.
- The paper positions RISEBench as a foundation for systematic assessment and identifies reasoning-driven editing weaknesses for future improvement.
2 RISEBENCH
RISEBench organizes reasoning-intensive visual editing into four categories and evaluates outputs across instruction understanding, appearance preservation, and plausibility. Its benchmark construction spans diverse temporal, causal, spatial, and logical tasks, while its evaluation pipeline combines human and LMM-based assessment.
- Benchmark Construction: RISEBench targets temporal, causal, spatial, and logical reasoning through diverse test cases pairing input images with reasoning-driven editing instructions.The task distribution is presented in Figure 3.
- Temporal Reasoning: Temporal tasks assess changes over time across life progression, environmental cycles, material state change, and societal transformation.The design varies dimensions including scale, direction, and object to capture fine-grained temporal dynamics.
- Causal Reasoning: Causal tasks evaluate how external forces or events induce changes in object states and require knowledge of material properties, physical principles, and cause-effect relationships.
- Spatial Reasoning: Spatial tasks evaluate preservation and manipulation of object relationships using geometric, structural, three-dimensional, and perspective reasoning.Subcategories include component assembly, object arrangement, viewpoint generation, structural reasoning, and layout reasoning.
- Logical Reasoning: Logical tasks require structured, rule-based inference from visual inputs and include puzzles and other logical challenges.The paper contrasts these tasks with categories centered on physical or commonsense understanding in natural images.
- Evaluation Pipeline: Evaluation scores instruction reasoning, appearance consistency, and visual plausibility through human evaluation and LMM-based assessment.The dimensions cover instruction execution, preservation of unrelated visual elements, and coherent, realistic outputs without artifacts or physical-law violations.
3 Experiments
Experiments compare diverse visual editing models on RISEBench using LMM-based scores and human validation. GPT-4o-Image leads overall but remains weak on logical reasoning, while model capabilities vary substantially across evaluation dimensions.
- Model Evaluation: The study evaluates visual editing models spanning diffusion-based, autoregressive, hybrid, and proprietary architectures.Flux1.0-Canny, EMU2, OmniGen, BAGEL, Step1X-Edit, and proprietary models are included.
- Main Results: 28.9% accuracy makes GPT-4o-Image the highest-performing model, but its score remains low on reasoning-intensive editing tasks.Gemini-2.0-Flash-Experimental and Preview follow with 13.3% and 9.4%, respectively.
- Reasoning Categories: GPT-4o-Image exceeds 30% accuracy on temporal, causal, and spatial reasoning but reaches only 10.6% on logical reasoning.Logical reasoning involves complex puzzles and intricate instructions and is identified as a major bottleneck.
- Evaluation Dimensions: GPT-4o-Image leads Instruction Reasoning, Appearance Consistency, and Visual Plausibility, while other models show dimension-specific weaknesses.BAGEL shows some instruction and appearance understanding but low visual plausibility; OmniGen has the lowest visual plausibility, and Step1X scores 41.5 in Appearance Consistency while lacking instruction understanding.
- Qualitative Analysis: Qualitative outputs show GPT-4o-Image preserving original content despite ambiguous instructions, whereas Gemini introduces artifacts and other models struggle with complex instructions.Gemini may add extraneous elements, omit critical content, or produce distorted reconstructions when instructions are misinterpreted.
- LMM-as-a-Judge Validity: Human and LMM judgments align closely for high-quality and failed outputs, but agreement decreases for intermediate scores.Overall MAEs are 0.5, 0.7, and 0.4 for Instruction Reasoning, Appearance Consistency, and Visual Plausibility; intermediate-score disagreement is attributed to subjective criteria.
4 Conclusion
RISEBench is introduced as a dedicated benchmark for reasoning-informed visual editing across four reasoning types and three evaluation dimensions. Experiments find GPT-4o-Image substantially ahead of other models, while logical reasoning remains a notable weakness.
- Benchmark Scope: RISEBench evaluates temporal, causal, spatial, and logical reasoning through instruction reasoning, appearance consistency, and generation plausibility.The benchmark provides a structured framework for assessing reasoning-informed visual editing in multimodal models.
- Main Finding: GPT-4o-Image substantially outperforms open-source and proprietary counterparts on RISEBench.The conclusion reports this as the central comparative result of the experiments.
- Future Direction: Logical reasoning remains a key area for future research and model development.Even advanced models continue to exhibit notable shortcomings on logical reasoning tasks.
A Related Work
Diffusion-based image editing has advanced through training-free and training-based methods, but limited fine-grained semantic understanding restricts performance on complex instructions requiring higher-order reasoning.
- Training-free methods guide diffusion denoising using editing instructions through noise reversal, attention control, or image blending.
- Training-based methods fine-tune pretrained text-to-image diffusion models on paired edited-image datasets and achieve superior editing performance.
- Diffusion-based editing models often struggle with complex, fine-grained instructions that require higher-order reasoning.
A.2 Unified Large Multi-Modality Models
Unified LMMs combine textual and visual inputs for cross-modal generation and understanding, but whether these capabilities mutually enhance one another remains unresolved. Commercial models’ reasoning-based image generation suggests a promising direction.
- Unified LMMs are designed to process textual and visual inputs while supporting cross-modal generation and understanding.
- Whether understanding and generation mutually enhance each other within unified LMMs remains an open question.
- GPT-4o-Image and Gemini-2.0-Flash demonstrate impressive reasoning-based image generation capabilities.
A.3 Text-to-Image Generation Evaluation
Text-to-image evaluation has progressed from distribution-level FID to foundation-model-based semantic alignment metrics, but reasoning-based visual editing remains insufficiently evaluated.
- FID measures distance between generated and target distributions but does not measure per-image alignment with instructions.
- Foundation-model metrics based on CLIP or object detectors were introduced to better measure semantic alignment in text-to-image generation.
- Few evaluation methods explicitly measure reasoning capabilities in visual editing, including multimodal understanding and consistency preservation.
B Data Source of RISEBench
RISEBench draws input images from generated, 3D-rendered, existing-dataset, and permissively licensed internet sources. The provided table passage reports GPT-4o-Image’s strengths and weaknesses across subtasks.
- Data sources: RISEBench sources inputs from images generated by image-generation models.
- Data sources: It also uses images rendered from 3D environments with Blender.
- Data sources: Additional inputs come from existing datasets and benchmarks, plus internet images collected under permissive licenses.
- Subtask performance: GPT-4o-Image performs well in common scenarios but struggles with Chemical, Biology, Physics, Pattern Prediction, and Puzzle Solving subtasks.
C Performance across Subtasks
GPT-4o-Image performs well on instruction-following edits in common scenarios, but its performance declines on tasks requiring deeper understanding. This pattern is also reflected in its broader reasoning-task evaluation.
- GPT-4o-Image performs strongly on Life Progression, Structural Deformation, and Viewpoint Generation tasks.
- GPT-4o-Image’s performance significantly declines on tasks requiring deeper or more difficult understanding.
- GPT-4o-Image can effectively handle tasks in common scenarios, but struggles as the required visual understanding becomes more demanding.
D Score Distribution of Model Outputs
GPT-4o-Image and Gemini-Series models receive favorable scores across the three evaluation metrics, while other models perform substantially more weakly. Human annotation is supported by an interactive interface.
- GPT-4o-Image and Gemini-Series models consistently achieve a high proportion of favorable scores across all three evaluation metrics.The metrics are Instruction Reasoning, Appearance Consistency, and Visual Plausibility.
- Other evaluated models perform notably weaker, particularly on instruction reasoning and appearance consistency.
- Human annotators use an interactive interface to support the annotation process.
F Limitations
The authors characterize RISEBench as an initial benchmark whose task categories may be incomplete and whose dataset contains only 360 questions. These constraints define the benchmark’s current scope.
- RISEBench’s included task categories may not be exhaustive because the benchmark is still in its initial stages.
- The dataset contains only 360 questions, which the authors describe as not substantial.
- The benchmark is presented as an initial effort to evaluate reasoning-informed image editing capabilities.