There has been a recent uptick in the development of general-purpose multimodal AI assistants capable of following visual and written directions, thanks to the remarkable success of Large Language Models (LLMs). By utilizing the impressive reasoning capabilities of LLMs and information found in huge alignment corpus (such as image-text pairs), they demonstrate the immense potential…

The intersection of artificial intelligence and creativity has witnessed an exceptional breakthrough in the form of text-to-image (T2I) diffusion models. These models, which convert textual descriptions into visually compelling images, have broadened the horizons of digital art, content creation, and more. Yet this rapidly evolving area of Personalized T2I generation study grapples with several core…

Deep learning has revolutionized view synthesis in computer vision, offering diverse approaches like NeRF and end-to-end style architectures. Traditionally, 3D modeling methods like voxels, point clouds, or meshes were employed. NeRF-based techniques implicitly represent 3D scenes using MLPs. Recent advancements focus on image-to-image approaches, generating novel views from collections of scene images. These methods often…

MoD-SLAM is a state-of-the-art method for Simultaneous Localization And Mapping (SLAM) systems. In SLAM systems, it is challenging to achieve real-time, accurate, and scalable dense mapping. To address these challenges, researchers have introduced a novel method focusing on unbounded scenes using only RGB images. Existing neural SLAM methods often rely on RGB-D input which leads…