Shape-Preserving Generation of Food Images for Automatic Dietary Assessment

Guangzong Chen, Zhi-Hong Mao, Mingui Sun, Kangni Liu, Wenyan Jia; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3721-3731

Traditional dietary assessment methods heavily rely on self-reporting which is time-consuming and prone to bias. Recent advancements in Artificial Intelligence (AI) have revealed new possibilities for dietary assessment particularly through analysis of food images. Recognizing foods and estimating food volumes from images are known as the key procedures for automatic dietary assessment. However both procedures required large amounts of training images labeled with food names and volumes which are currently unavailable. Alternatively recent studies have indicated that training images can be artificially generated using Generative Adversarial Networks (GANs). Nonetheless convenient generation of large amounts of food images with known volumes remain a challenge with the existing techniques. In this work we present a simple GAN-based neural network architecture for conditional food image generation. The shapes of the food and container in the generated images closely resemble those in the reference input image. Our experiments demonstrate the realism of the generated images and shape-preserving capabilities of the proposed framework.