{"title":"用于三维点云的生成式人工智能","authors":"Minhas Kamal, Balakrishnan Prabhakaran","doi":"10.1109/mmul.2024.3413395","DOIUrl":null,"url":null,"abstract":"Generative AI refers to algorithms and techniques designed to generate text, images, videos, or other data, typically in response to prompts. These algorithms leverage large generative models that learn the patterns and structures of the media data (text, images, or videos) provided during training and then generate new media data that have analogous characteristics. Much of the recent research has gone into applying generative AI for text and 2-D image data. However, generative AI for 3-D models, especially 3-D point cloud data (PCD), has compelling applications in virtual reality content generation, gaming, and product design and manufacturing, but it introduces a multitude of research challenges.","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"25 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generative AI for 3-D Point Clouds\",\"authors\":\"Minhas Kamal, Balakrishnan Prabhakaran\",\"doi\":\"10.1109/mmul.2024.3413395\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generative AI refers to algorithms and techniques designed to generate text, images, videos, or other data, typically in response to prompts. These algorithms leverage large generative models that learn the patterns and structures of the media data (text, images, or videos) provided during training and then generate new media data that have analogous characteristics. Much of the recent research has gone into applying generative AI for text and 2-D image data. However, generative AI for 3-D models, especially 3-D point cloud data (PCD), has compelling applications in virtual reality content generation, gaming, and product design and manufacturing, but it introduces a multitude of research challenges.\",\"PeriodicalId\":13240,\"journal\":{\"name\":\"IEEE MultiMedia\",\"volume\":\"25 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE MultiMedia\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/mmul.2024.3413395\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE MultiMedia","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mmul.2024.3413395","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Generative AI refers to algorithms and techniques designed to generate text, images, videos, or other data, typically in response to prompts. These algorithms leverage large generative models that learn the patterns and structures of the media data (text, images, or videos) provided during training and then generate new media data that have analogous characteristics. Much of the recent research has gone into applying generative AI for text and 2-D image data. However, generative AI for 3-D models, especially 3-D point cloud data (PCD), has compelling applications in virtual reality content generation, gaming, and product design and manufacturing, but it introduces a multitude of research challenges.
期刊介绍:
The magazine contains technical information covering a broad range of issues in multimedia systems and applications. Articles discuss research as well as advanced practice in hardware/software and are expected to span the range from theory to working systems. Especially encouraged are papers discussing experiences with new or advanced systems and subsystems. To avoid unnecessary overlap with existing publications, acceptable papers must have a significant focus on aspects unique to multimedia systems and applications. These aspects are likely to be related to the special needs of multimedia information compared to other electronic data, for example, the size requirements of digital media and the importance of time in the representation of such media. The following list is not exhaustive, but is representative of the topics that are covered: Hardware and software for media compression, coding & processing; Media representations & standards for storage, editing, interchange, transmission & presentation; Hardware platforms supporting multimedia applications; Operating systems suitable for multimedia applications; Storage devices & technologies for multimedia information; Network technologies, protocols, architectures & delivery techniques intended for multimedia; Synchronization issues; Multimedia databases; Formalisms for multimedia information systems & applications; Programming paradigms & languages for multimedia; Multimedia user interfaces; Media creation integration editing & management; Creation & modification of multimedia applications.