Panoptic-Based Image Synthesis
Aysegul Dundar, Karan Sapra, Guilin Liu, Andrew Tao, Bryan Catanzaro
2020年份
8顶会引用
摘要
Panoptic maps Baseline Ours Ours enlarged Baseline enlarged Figure 1. Unlike previous methods that rely on semantic and boundary label maps to synthesize images, our model uses panoptic maps. It generates instances with clear separation even in cluttered scenes where multiple instances occlude each other. The forth and fifth column show zoomed in patches from the second and third column images to highlight the boundaries between instances, where previous methods tend to blend instances together.
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引用它的顶会 Paper8
- Dual Contrastive Loss and Attention for GANsNing Yu, Guilin Liu, Aysegul Dundar, Andrew Tao 等ICCV 2021 · 被引用 69 次
- Diverse Inpainting and Editing with GAN InversionAhmet Burak Yildirim, Hamza Pehlivan, Bahri Batuhan Bilecen, Aysegul DundarICCV 2023 · 被引用 35 次
- Neural FFTs for Universal Texture Image SynthesisMorteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu 等NeurIPS 2020 · 被引用 31 次
- Semantic-shape Adaptive Feature Modulation for Semantic Image SynthesisZhengyao Lv, Xiaoming Li, Zhenxing Niu, Bing Cao 等CVPR 2022 · 被引用 26 次
- Reference-Based 3D-Aware Image Editing with TriplanesBahri Batuhan Bilecen, Yigit Yalin, Ning Yu, Aysegul DundarCVPR 2025
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