1 code implementation • 22 Oct 2021 • Fangda Han, Guoyao Hao, Ricardo Guerrero, Vladimir Pavlovic
To synthesize a pizza image with view attributesoutside the range of natural training images, we design a CGI pizza dataset PizzaView using 3D pizza models and employ it to train a view attribute regressor to regularize the generation process, bridging the real and CGI training datasets.
1 code implementation • 22 Sep 2021 • Malihe Alikhani, Fangda Han, Hareesh Ravi, Mubbasir Kapadia, Vladimir Pavlovic, Matthew Stone
Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model.
1 code implementation • 4 Dec 2020 • Fangda Han, Guoyao Hao, Ricardo Guerrero, Vladimir Pavlovic
Because of the complex nature of the multilabel image generation problem, we also regularize synthetic image by predicting the corresponding ingredients as well as encourage the discriminator to distinguish between matched image and mismatched image.
no code implementations • 17 Oct 2020 • Jiatong Li, Fangda Han, Ricardo Guerrero, Vladimir Pavlovic
Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems.
1 code implementation • 25 Feb 2020 • Fangda Han, Ricardo Guerrero, Vladimir Pavlovic
In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual list of its ingredients.
1 code implementation • 9 May 2019 • Fangda Han, Ricardo Guerrero, Vladimir Pavlovic
In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual descriptions of its ingredients.
no code implementations • 25 Mar 2017 • Long Zhao, Fangda Han, Xi Peng, Xun Zhang, Mubbasir Kapadia, Vladimir Pavlovic, Dimitris N. Metaxas
We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame.