PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Li, Junxian Liu, Kai Chen, Leyang Wang, Weida Wang, Zhixin Xu, Jiaqi Li, Fan Pei, Renjing Kong, Linghe Zhang, Yulun |
| author_facet | Li, Junxian Liu, Kai Chen, Leyang Wang, Weida Wang, Zhixin Xu, Jiaqi Li, Fan Pei, Renjing Kong, Linghe Zhang, Yulun |
| contents | Unified multimodal models (UMMs) have shown impressive capabilities in generating natural images and supporting multimodal reasoning. However, their potential in supporting computer-use planning tasks, which are closely related to our lives, remain underexplored. Image generation and editing in computer-use tasks require capabilities like spatial reasoning and procedural understanding, and it is still unknown whether UMMs have these capabilities to finish these tasks or not. Therefore, we propose PlanViz, a new benchmark designed to evaluate image generation and editing for computer-use tasks. To achieve the goal of our evaluation, we focus on sub-tasks which frequently involve in daily life and require planning. Specifically, three representative sub-tasks are designed: route planning, work diagramming, and web&UI displaying. We address challenges in data quality ensuring by curating human-annotated questions and reference images, and a quality control process. For detailed and exact evaluation, a task-adaptive score, PlanScore, is proposed. The score helps understanding the correctness, visual quality and efficiency of generated images. Through experiments, we highlight key limitations and opportunities for future research on this topic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06663 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks Li, Junxian Liu, Kai Chen, Leyang Wang, Weida Wang, Zhixin Xu, Jiaqi Li, Fan Pei, Renjing Kong, Linghe Zhang, Yulun Computer Vision and Pattern Recognition Unified multimodal models (UMMs) have shown impressive capabilities in generating natural images and supporting multimodal reasoning. However, their potential in supporting computer-use planning tasks, which are closely related to our lives, remain underexplored. Image generation and editing in computer-use tasks require capabilities like spatial reasoning and procedural understanding, and it is still unknown whether UMMs have these capabilities to finish these tasks or not. Therefore, we propose PlanViz, a new benchmark designed to evaluate image generation and editing for computer-use tasks. To achieve the goal of our evaluation, we focus on sub-tasks which frequently involve in daily life and require planning. Specifically, three representative sub-tasks are designed: route planning, work diagramming, and web&UI displaying. We address challenges in data quality ensuring by curating human-annotated questions and reference images, and a quality control process. For detailed and exact evaluation, a task-adaptive score, PlanScore, is proposed. The score helps understanding the correctness, visual quality and efficiency of generated images. Through experiments, we highlight key limitations and opportunities for future research on this topic. |
| title | PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.06663 |