A dive into the different ways that Artificial Intelligence (AI) can and is being used in the VFX industry.
We propose an interactive GAN-based sketch-to-image translation method that helps novice users easily create images of simple objects. The user starts with a sparse sketch and a desired object category, and the network then recommends its plausible completion(s) and shows a corresponding synthesized image. This enables a feedback loop, where the user can edit the sketch based on the network's recommendations, while the network is able to better synthesize the image that the user might have in mind. In order to use a single model for a wide array of object classes, we introduce a gating-based approach for class conditioning, which allows us to generate distinct classes without feature mixing, from a single generator network.
Project Contributors: Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H.S. Torr, Eli Shechtman.
SPEAKER
Arnab Ghosh
Machine Learning Engineer // Snap Inc.
Arnab works in Snapchat's Computer Vision team called CameOS. Previously he worked for the Applied AI team at ARM working on Speaker Verification.
Arnab studied at St. Cross College Oxford at the Department of Engineering Science. His research involved Generative Modeling Techniques based on Deep Learning for Computer Vision applications such as Video Generation and 3D generation.
A dive into the different ways that Artificial Intelligence (AI) can and is being used in the VFX industry.
We propose an interactive GAN-based sketch-to-image translation method that helps novice users easily create images of simple objects. The user starts with a sparse sketch and a desired object category, and the network then recommends its plausible completion(s) and shows a corresponding synthesized image. This enables a feedback loop, where the user can edit the sketch based on the network's recommendations, while the network is able to better synthesize the image that the user might have in mind. In order to use a single model for a wide array of object classes, we introduce a gating-based approach for class conditioning, which allows us to generate distinct classes without feature mixing, from a single generator network.
Project Contributors: Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H.S. Torr, Eli Shechtman.
SPEAKER
Arnab Ghosh
Machine Learning Engineer // Snap Inc.
Arnab works in Snapchat's Computer Vision team called CameOS. Previously he worked for the Applied AI team at ARM working on Speaker Verification.
Arnab studied at St. Cross College Oxford at the Department of Engineering Science. His research involved Generative Modeling Techniques based on Deep Learning for Computer Vision applications such as Video Generation and 3D generation.
