Ayush Sarkar

Viraj Shah, Ayush Sarkar, Sudharsan Krishnakumar Anitha et al.

Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference style. Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference image. However, such methods cannot generate multiple stylizations without fine-tuning a new model for each style separately. In this work, we present a MultiStyleGAN method that is capable of producing multiple different stylizations at once by fine-tuning a single generator. The key component of our method is a learnable transformation module called Style Transformation Network. It takes latent codes as input, and learns linear mappings to different regions of the latent space to produce distinct codes for each style, resulting in a multistyle space. Our model inherently mitigates overfitting since it is trained on multiple styles, hence improving the quality of stylizations. Our method can learn upwards of $120$ image stylizations at once, bringing $8\times$ to $60\times$ improvement in training time over recent competing methods. We support our results through user studies and quantitative results that indicate meaningful improvements over existing methods.

Ayush Sarkar, Hanlin Mai, Amitabh Mahapatra et al.

Generative models can produce impressively realistic images. This paper demonstrates that generated images have geometric features different from those of real images. We build a set of collections of generated images, prequalified to fool simple, signal-based classifiers into believing they are real. We then show that prequalified generated images can be identified reliably by classifiers that only look at geometric properties. We use three such classifiers. All three classifiers are denied access to image pixels, and look only at derived geometric features. The first classifier looks at the perspective field of the image, the second looks at lines detected in the image, and the third looks at relations between detected objects and shadows. Our procedure detects generated images more reliably than SOTA local signal based detectors, for images from a number of distinct generators. Saliency maps suggest that the classifiers can identify geometric problems reliably. We conclude that current generators cannot reliably reproduce geometric properties of real images.