Fatih Porikli, VP of Technology at @QCOMResearch, highlights the following CVPR papers: 🪩 DisCo - a reinforcement learning approach that uses Group Relative Policy Optimization (GRPO) to improve identity diversity in generated images using rewards such as intra-image diversity, inter-image diversity, count accuracy, and image quality 🎨 Ar2Can - a novel "architect and artist" framework that separates scene planning and composition from image rendering, similar to how a human artist might work, to produce more controllable and coherent generations. 🖼️ PixelRush tackles the challenge of generating high-resolution images efficiently on mobile devices using cascade upsampling, latent-space refinement, patchification, and semantically guided noise injection to produce high-quality images without visible artifacts. 🖌️ InverFill tackles the challenge of image inpainting using inverted, semantically steered noise to preserve the background and eliminate boundary artifacts 🎥 Dig into the papers here: