Cookgan: Causality based text-to-image synthesis
This paper addresses the problem of text-to-image synthesis from a new perspective, i.e., the cause-and-effect chain in image generation. Causality is a common phenomenon in cooking. The dish appearance changes depending on the cooking actions and ingredients. The challenge of synthesis is that a ge...
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Main Authors: | , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2020
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Online Access: | https://ink.library.smu.edu.sg/sis_research/6484 https://ink.library.smu.edu.sg/context/sis_research/article/7487/viewcontent/Zhu_CookGAN_Causality_Based_Text_to_Image_Synthesis_CVPR_2020_paper.pdf |
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Institution: | Singapore Management University |
Language: | English |
Summary: | This paper addresses the problem of text-to-image synthesis from a new perspective, i.e., the cause-and-effect chain in image generation. Causality is a common phenomenon in cooking. The dish appearance changes depending on the cooking actions and ingredients. The challenge of synthesis is that a generated image should depict the visual result of action-on-object. This paper presents a new network architecture, CookGAN, that mimics visual effect in causality chain, preserves fine-grained details and progressively upsamples image. Particularly, a cooking simulator sub-network is proposed to incrementally make changes to food images based on the interaction between ingredients and cooking methods over a series of steps. Experiments on Recipe1M verify that CookGAN manages to generate food images with reasonably impressive inception score. Furthermore, the images are semantically interpretable and manipulable. |
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