Variation-Aware Semantic Image Synthesis

• URL: http://arxiv.org/abs/2301.10551v1
• Date: Wed, 25 Jan 2023 12:35:17 GMT
• Title: Variation-Aware Semantic Image Synthesis
• Authors: Mingle Xu and Jaehwan Lee and Sook Yoon and Hyongsuk Kim and Dong Sun Park
• Abstract summary: We introduce two simple methods to achieve variation-aware semantic image synthesis (VASIS) with a higher intra-class variation, semantic noise and position code. Our models generate more natural images and achieves slightly better FIDs and/or mIoUs than the counterparts.
• Score: 5.232306238197685
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years. Although the diversity in image-level has been discussed heavily, class-level mode collapse widely exists in current algorithms. Therefore, we declare a new requirement for SIS to achieve more photorealistic images, variation-aware, which consists of inter- and intra-class variation. The inter-class variation is the diversity between different semantic classes while the intra-class variation stresses the diversity inside one class. Through analysis, we find that current algorithms elusively embrace the inter-class variation but the intra-class variation is still not enough. Further, we introduce two simple methods to achieve variation-aware semantic image synthesis (VASIS) with a higher intra-class variation, semantic noise and position code. We combine our method with several state-of-the-art algorithms and the experimental result shows that our models generate more natural images and achieves slightly better FIDs and/or mIoUs than the counterparts. Our codes and models will be publicly available.

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