The 2021 IEEE Winter Conference on Applications of Computer Vision (WACV 2021) announced that a paper co-authored by Rushil Anirudh received the conference’s Best Paper Honorable Mention award based on its potential impact to the field. The paper, titled “Generative Patch Priors for Practical Compressive Image Recovery,” introduces a new kind of prior—a characterization of the space of natural images—for compressive image recovery that is trained on patches of images instead of full-sized images. Unlike existing generative methods that are applicable only to images similar to the training dataset—i.e., similar kinds of objects, image sizes or aspect ratios—the generative patch prior (GPP) can recover a wide variety of natural images and compares favorably to other existing methods, researchers said. Anirudh presented the paper on behalf of the group during an awards session hosted by the virtual conference, the premier event of its kind in the world. The conference received about 1,100 paper submissions—only 5 were honored with awards. The code used in the paper is available on the open source repository GPP on GitHub.