Cookgan: Causality based text-to-image synthesis

• Main Authors: ZHU, Bin, NGO, Chong-wah
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Computer Sciences; Graphics and Human Computer Interfaces
• 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
• Institution: Singapore Management University

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.