Simultaneous Multi‐Attribute Image‐to‐Image Translation Using Parallel Latent Transform Networks. (24th November 2020)

• Record Type: Journal Article
• Authors: Xu, Sen‐Zhe; Lai, Yu‐Kun
• Is Part Of: Computer graphics forum. Volume 39:Number 7(2020)
• Page Start: 531
• Page End: 542
• Publication Date: 2020-11-24
• Subjects: CCS Concepts -- Computing methodologies $Ar Image manipulation; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14165
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: Image‐to‐image translation has been widely studied. Since real‐world images can often be described by multiple attributes, it is useful to manipulate them at the same time. However, most methods focus on transforming between two domains, and when they chain multiple single attribute transform networks together, the results are affected by the order of chaining, and the performance drops with the out‐of‐domain issue for intermediate results. Existing multi‐domain transfer methods mostly manipulate multiple attributes by adding a list of attribute labels to the network feature, but they also suffer from interference of different attributes, and perform worse when multiple attributes are manipulated. We propose a novel approach to multi‐attribute image‐to‐image translation using several parallel latent transform networks, where multiple attributes are manipulated in parallel and simultaneously, which eliminates both issues. To avoid the interference of different attributes, we introduce a novel soft independence constraint for the changes caused by different attributes. Extensive experiments show that our method outperforms state‐of‐the‐art methods.