{"title":"IEEE Computer Society Career Center","authors":"","doi":"10.1109/mcg.2024.3367609","DOIUrl":"https://doi.org/10.1109/mcg.2024.3367609","url":null,"abstract":"","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"5 4 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IEEE Computer Society Has You Covered!","authors":"","doi":"10.1109/mcg.2024.3367628","DOIUrl":"https://doi.org/10.1109/mcg.2024.3367628","url":null,"abstract":"","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"142 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Call for Articles: IT Professional","authors":"","doi":"10.1109/mcg.2024.3367613","DOIUrl":"https://doi.org/10.1109/mcg.2024.3367613","url":null,"abstract":"","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"2 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Testing the Capability of AI Art Tools for Urban Design.","authors":"Connor Phillips, Junfeng Jiao, Emmalee Clubb","doi":"10.1109/MCG.2024.3356169","DOIUrl":"10.1109/MCG.2024.3356169","url":null,"abstract":"<p><p>This study aimed to evaluate the performance of three artificial intelligence (AI) image synthesis models, Dall-E 2, Stable Diffusion, and Midjourney, in generating urban design imagery based on scene descriptions. A total of 240 images were generated and evaluated by two independent professional evaluators using an adapted sensibleness and specificity average metric. The results showed significant differences between the three AI models, as well as differing scores across urban scenes, suggesting that some projects and design elements may be more challenging for AI art generators to represent visually. Analysis of individual design elements showed high accuracy in common features like skyscrapers and lawns, but less frequency in depicting unique elements such as sculptures and transit stops. AI-generated urban designs have potential applications in the early stages of exploration when rapid ideation and visual brainstorming are key. Future research could broaden the style range and include more diverse evaluative metrics. The study aims to guide the development of AI models for more nuanced and inclusive urban design applications, enhancing tools for architects and urban planners.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":"37-45"},"PeriodicalIF":1.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139503006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"NeRF-In: Free-Form Inpainting for Pretrained NeRF With RGB-D Priors.","authors":"I-Chao Shen, Hao-Kang Liu, Bing-Yu Chen","doi":"10.1109/MCG.2023.3336224","DOIUrl":"10.1109/MCG.2023.3336224","url":null,"abstract":"<p><p>Neural radiance field (NeRF) has emerged as a versatile scene representation. However, it is still unintuitive to edit a pretrained NeRF because the network parameters and the scene appearance are often not explicitly associated. In this article, we introduce the first framework that enables users to retouch undesired regions in a pretrained NeRF scene without accessing any training data and category-specific data prior. The user first draws a free-form mask to specify a region containing the unwanted objects over an arbitrary rendered view from the pretrained NeRF. Our framework transfers the user-drawn mask to other rendered views and estimates guiding color and depth images within transferred masked regions. Next, we formulate an optimization problem that jointly inpaints the image content in all masked regions by updating NeRF's parameters. We demonstrate our framework on diverse scenes and show it obtained visually plausible and structurally consistent results using less user manual efforts.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":"100-109"},"PeriodicalIF":1.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138453100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"How Text-to-Image Generative AI Is Transforming Mediated Action.","authors":"Henriikka Vartiainen, Matti Tedre","doi":"10.1109/MCG.2024.3355808","DOIUrl":"10.1109/MCG.2024.3355808","url":null,"abstract":"<p><p>This article examines the intricate relationship between humans and text-to-image generative models (generative artificial intelligence/genAI) in the realm of art. The article frames that relationship in the theory of mediated action-a well-established theory that conceptualizes how tools shape human thoughts and actions. The article describes genAI systems as learning, cocreating, and communicating, multimodally capable hybrid systems that distill and rely on the wisdom and creativity of massive crowds of people and can sometimes surpass them. Those systems elude the theoretical description of the role of tools and locus of control in mediated action. The article asks how well the theory can accommodate both the transformative potential of genAI tools in creative fields and art, and the ethics of the emergent social dynamics it generates. The article concludes by discussing the fundamental changes and broader implications that genAI brings to the realm of mediated action and, ultimately, to the very fabric of our daily lives.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":"12-22"},"PeriodicalIF":1.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139576009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Databiting: Lightweight, Transient, and Insight Rich Exploration of Personal Data.","authors":"Bradley Rey, Bongshin Lee, Eun Kyoung Choe, Pourang Irani, Theresa-Marie Rhyne","doi":"10.1109/MCG.2024.3353888","DOIUrl":"https://doi.org/10.1109/MCG.2024.3353888","url":null,"abstract":"<p><p>As mobile and wearable devices are becoming increasingly powerful, access to personal data is within reach anytime and anywhere. Currently, methods of data exploration while on-the-go and in-situ are, however, often limited to glanceable and micro visualizations, which provide narrow insight. In this article, we introduce the notion of databiting, the act of interacting with personal data to obtain richer insight through lightweight and transient exploration. We focus our discussion on conceptualizing databiting and arguing its potential values. We then discuss five research considerations that we deem important for enabling databiting: contextual factors, interaction modalities, the relationship between databiting and other forms of exploration, personalization, and evaluation challenges. We envision this line of work in databiting could enable people to easily gain meaningful personal insight from their data anytime and anywhere.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"44 2","pages":"65-72"},"PeriodicalIF":1.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}