人类图像生成:全面调查

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-05-22 DOI:10.1145/3665869
Zhen Jia, Zhang Zhang, Liang Wang, Tieniu Tan
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引用次数: 0

摘要

随着深度生成模型的发展,图像和视频合成因其巨大的学术和应用价值,已成为计算机视觉和机器学习领域一个蓬勃发展的课题。作为日常生活中最常见的对象类别之一,许多研究人员都致力于合成高保真人体图像,并基于各种模型、任务设置和应用进行了大量研究。因此,有必要对这些不同的人体图像生成方法进行全面概述。本文将人类图像生成技术分为三种范式,即数据驱动法、知识引导法和混合法。针对每种范式,我们都介绍了最具代表性的模型和相应的变体,并从模型架构的角度总结了不同方法的优势和特点。此外,还总结了文献中主要的公共人类图像数据集和评估指标。此外,由于合成人体图像具有广泛的应用潜力,还介绍了合成人体图像的典型下游用途。最后,讨论了人类图像生成所面临的挑战和潜在机遇,为未来研究提供启示。
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Human Image Generation: A Comprehensive Survey

Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value. Many researchers have been devoted to synthesizing high-fidelity human images as one of the most commonly seen object categories in daily lives, where a large number of studies are performed based on various models, task settings and applications. Thus, it is necessary to give a comprehensive overview on these variant methods on human image generation. In this paper, we divide human image generation techniques into three paradigms, i.e., data-driven methods, knowledge-guided methods and hybrid methods. For each paradigm, the most representative models and the corresponding variants are presented, where the advantages and characteristics of different methods are summarized in terms of model architectures. Besides, the main public human image datasets and evaluation metrics in the literature are summarized. Furthermore, due to the wide application potentials, the typical downstream usages of synthesized human images are covered. Finally, the challenges and potential opportunities of human image generation are discussed to shed light on future research.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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