3 accepted papers
Evaluation of synthetic images is important for both model development and selection. An ideal evaluation should be specific, accurate and aligned with human perception. This paper addresses the problem of evaluating realism of objects in synthetic images. Although methods has been proposed to evalu…
We derive a method that yields highly accurate semantic segmentation maps without the use of any additional neural network, layers, manually annotated training data, or supervised training. Our method is based on the observation that the correlation of a set of pixels belonging to the same semantic…
"We present a method for finding paths in a deep generative model’s latent space that can maximally vary one set of image features while holding others constant. Crucially, unlike past traversal approaches, ours can manipulate arbitrary multidimensional features of an image such as facial identity a…