Human Image Generation: A Comprehensive Survey

• URL: http://arxiv.org/abs/2212.08896v3
• Date: Fri, 24 May 2024 03:33:47 GMT
• Title: Human Image Generation: A Comprehensive Survey
• Authors: Zhen Jia, Zhang Zhang, Liang Wang, Tieniu Tan,
• Abstract summary: In this paper, we divide human image generation techniques into three paradigms, i.e., data-driven methods, knowledge-guided methods and hybrid methods. The advantages and characteristics of different methods are summarized in terms of model architectures. Due to the wide application potentials, the typical downstream usages of synthesized human images are covered.
• Score: 44.204029557298476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Human-Centric Foundation Models: Perception, Generation and Agentic Modeling [79.97999901785772]
  Human-centric Foundation Models unify diverse human-centric tasks into a single framework. We present a comprehensive overview of HcFMs by proposing a taxonomy that categorizes current approaches into four groups. This survey aims to serve as a roadmap for researchers and practitioners working towards more robust, versatile, and intelligent digital human and embodiments modeling.
  arXiv  Detail & Related papers  (2025-02-12T16:38:40Z)
• Human Multi-View Synthesis from a Single-View Model:Transferred Body and Face Representations [7.448124739584319]
  We propose an innovative framework that leverages transferred body and facial representations for multi-view human synthesis. Specifically, we use a single-view model pretrained on a large-scale human dataset to develop a multi-view body representation. Our approach outperforms the current state-of-the-art methods, achieving superior performance in multi-view human synthesis.
  arXiv  Detail & Related papers  (2024-12-04T04:02:17Z)
• Single Image, Any Face: Generalisable 3D Face Generation [59.9369171926757]
  We propose a novel model, Gen3D-Face, which generates 3D human faces with unconstrained single image input. To the best of our knowledge, this is the first attempt and benchmark for creating photorealistic 3D human face avatars from single images.
  arXiv  Detail & Related papers  (2024-09-25T14:56:37Z)
• Evaluating Multiview Object Consistency in Humans and Image Models [68.36073530804296]
  We leverage an experimental design from the cognitive sciences which requires zero-shot visual inferences about object shape. We collect 35K trials of behavioral data from over 500 participants. We then evaluate the performance of common vision models.
  arXiv  Detail & Related papers  (2024-09-09T17:59:13Z)
• HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors [47.62426718293504]
  HumanSplat predicts the 3D Gaussian Splatting properties of any human from a single input image. HumanSplat surpasses existing state-of-the-art methods in achieving photorealistic novel-view synthesis.
  arXiv  Detail & Related papers  (2024-06-18T10:05:33Z)
• Data Augmentation in Human-Centric Vision [54.97327269866757]
  This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
  arXiv  Detail & Related papers  (2024-03-13T16:05:18Z)
• Limitations of Face Image Generation [12.11955119100926]
  We study the efficacy and shortcomings of generative models in the context of face generation. We identify several limitations of face image generation that include faithfulness to the text prompt, demographic disparities, and distributional shifts. We present an analytical model that provides insights into how training data selection contributes to the performance of generative models.
  arXiv  Detail & Related papers  (2023-09-13T19:33:26Z)
• Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies [41.00383742615389]
  Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis.
  arXiv  Detail & Related papers  (2020-12-26T13:30:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.