NVIDIA Research Featured at European Conference on Computer Vision (ECCV) 2020

Researchers, developers, and engineers from all over the world are gathering virtually this year for the European Conference on Computer Vision (ECCV) 2020. 

Among the papers being presented by NVIDIA researchers at ECCV this year, COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder offers significant visual improvements to the popular GANimal demo featured on the AI Playground.

The researchers Kuniaki Saito, Kate Saenko, and Ming-Yu Liu present a model that effectively addressees previous content loss problems. The Image-to-Image translation successfully preserves the structure of the input content image, like a fluffy white puppy, while emulating the appearance of the unseen domain, a snow leopard. This generates a photorealistic translation of a puppy with a coat in the style of the snow leopard.

The researchers benchmarked their method using four datasets representing Carnivores, Mammals, Birds, and Motorbikes. This produced visually compelling images across a variety of subjects and poses.

For code and pretrained models, please check out https://nvlabs.github.io/COCO-FUNIT/

Additional papers being presented at ECCV by NVIDIA researchers and collaborators include: