GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments
StepFun；South China University of Technology（南方科技大学）；Peking University（北京大学）；Tsinghua University（清华大学）；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；The University of Chicago（芝加哥大学）；Nanyang Technological University（南洋理工大学）
An image generation model used as a graphical user interface (GUI) environment must produce not only a plausible screen but also the correct response to an action. Existing visual-generation benchmarks do not directly assess this combination of functional correctness and temporal consistency in GUIs. We introduce GUI-GenBench, a benchmark of 700 curated samples across five task suites: single-step transitions, multi-step planning, fictional-app generation, real-app trajectories, and coordinate-based grounding. We also introduce GUI-Score, a VLM-based metric that assesses Goal Achievement, Interaction Logic, Content Consistency, UI Plausibility, and Visual Quality. Evaluating 12 image generation models on Chinese and English subsets, we find that strong single-step performance does not consistently extend to coherent multi-step trajectories or accurate grounding. The highest aggregate GUI-Scores are 69.62 on the Chinese subset and 63.16 on the English subset. Qualitative analysis further identifies failures in text rendering, icon interpretation, and coordinate-conditioned transitions. Together, these results distinguish visual plausibility from functional reliability and identify limitations that must be addressed before generated GUIs can serve as dependable interaction environments.
Communication and Information Security Lab Shenzhen Graduate School, Peking University（北京大学深圳研究生院通信与信息安全实验室）
Image inpainting approaches have achieved significant progress with the help of deep neural networks. However, existing approaches mainly focus on leveraging the priori distribution learned by neural networks to produce a single inpainting result or further yielding multiple solutions, where the controllability is not well studied. This paper develops a novel image inpainting approach that enables users to customize the inpainting result by their own preference or memory. Specifically, our approach is composed of two stages that utilize the prior of neural network and user's guidance to jointl…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Significant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation. However, there lacks enough understanding of how a realistic image is generated by the deep representations of GANs from a random vector. This chapter gives a summary of recent works on interpreting deep generative models. The methods are categorized into the supervised, the unsupervised, and the embedding-guided approaches. We will see how the human-understandable concepts that emerge in the learned representation can be identified and used for interactive image generation and ed…
I2E: From Image Pixels to Actionable Interactive Environments for Text-Guided Image Editing
Huazhong University of Science and Technology（华中科技大学）；Kuaishou Technology（快手科技）；Central China Normal University（华中师范大学）；Tsinghua University（清华大学）；Shanghai AI Laboratory（上海人工智能实验室）
SocraticPO: Policy Optimization via Interactive Guidance
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd（iFLYTEK中央中国AI研究院，iFLYTEK公司）
The University of Tokyo（东京大学）；National Taiwan University（台湾大学）
Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain the desired results. Existing attempts add interactivity but require either tailored architectures or extra data. We present a human-in-the-optimization method that allows users to directly explore and search the latent vector space of generative image modeling. Our system provides multiple candidates by sampling the latent vector space, and the user selects the best blending weights within the subspace using multiple sliders. In additio…
Instituto de Telecomunicações (IT-IUL)（电信研究所（IT-IUL））；Instituto Universitário de Lisboa (Iscte-IUL)（里斯本大学学院（Iscte-IUL））
We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. Then it reviews each class of story generation approach depending on the used technology: story-schemas, analogy, rules, planning, evolutionary algorithms, implicit knowledge learning, and explicit knowledge learning. Before the concluding section, the article analyses the contributions of the reviewed work to impr…
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization
Alibaba Group（阿里巴巴集团）；Beijing Electronic Science and Technology Institute（北京电子科技研究所）；Nanjing University（南京大学）；Renmin University of China（中国人民大学）；Northeastern University（东北大学）；BraneMatrix AI；Nanyang Technological University（南洋理工大学）
SceneFoundry: Generating Interactive Infinite 3D Worlds
National Yang Ming Chiao Tung University（国家阳明交通大学）；National Tsing Hua University（国立清华大学）；NVIDIA AI Technology Center（NVIDIA AI技术中心）；National Taiwan University（国立台湾大学）
