Multi-scale information transport generative adversarial network for human pose transfer

Zhang, Jinsong, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Ma, Jian and Li, Kun 2024. Multi-scale information transport generative adversarial network for human pose transfer. Displays 84 , 102786. 10.1016/j.displa.2024.102786 Item availability restricted.

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Abstract

Human pose transfer, a challenging image generation task, aims to transfer a source image from one pose to another. Existing methods often struggle to preserve details in visible regions or predict reasonable pixels for invisible regions due to inaccurate correspondences. In this paper, we design a novel multi-scale information transport generative adversarial network, composed of Information Transport (IT) blocks to establish and refine the correspondences progressively. Specifically, we compute a transport matrix to warp the source image features by integrating an optimal transport solver in our proposed IT block, and use IT blocks to refine the correspondences in different resolutions to preserve rich details of the source image features. The experimental results and applications demonstrate the effectiveness of our proposed method. We further present an image-specific optimization using only a single image. The code is available for research purposes at https://github.com/Zhangjinso/OT-POSE.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2026-06-27
Publisher:	Elsevier
ISSN:	0141-9382
Date of First Compliant Deposit:	29 July 2024
Date of Acceptance:	19 June 2024
Last Modified:	09 Nov 2024 03:00
URI:	https://orca.cardiff.ac.uk/id/eprint/170150

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