Appearance and Pose-guided Human Generation: A Survey

Author:

Liao Fangjian1ORCID,Zou Xingxing2ORCID,Wong Waikeung2ORCID

Affiliation:

1. School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong SAR, China

2. School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong SAR, China and Laboratory for Artificial Intelligence in Design, Hong Kong SAR, China

Abstract

Appearance and pose-guided human generation is a burgeoning field that has captured significant attention. This subject’s primary objective is to transfer pose information from a target source to a reference image, enabling the generation of high-resolution images or videos that seamlessly link the virtual and real worlds, leading to novel trends and applications. This survey thoroughly illustrates the task of appearance and pose-guided human generation and comprehensively reviews mainstream methods. Specifically, it systematically discusses prior information, pose-based transformation modules, and generators, offering a comprehensive understanding and discussion of each mainstream pose transformation and generation process. Furthermore, the survey explores current applications and future challenges in the domain. Its ultimate goal is to serve as quick guidelines, providing practical assistance in human generation and its diverse applications.

Funder

Laboratory for Artificial Intelligence in Design

Innovation and Technology Fund, Hong Kong SAR

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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