Point cloud data collected in real-world applications are often incomplete. This is because they are observed from partial viewpoints, which capture only a specific perspective or angle, or due to occlusion and low resolution. Existing completion approaches rely on datasets of specific predefined objects to guide the completion of incomplete, and possibly noisy, point clouds. However, these approaches perform poorly with Out-Of-Distribution (OOD) objects, which are either absent from the dataset or poorly represented. In recent years, the field of text-guided image generation has made significant progress, leading to major breakthroughs in text guided shape generation. We describe an approach called SDS-Complete that uses a pre-trained text-to-image diffusion model and leverages the text semantic of a given incomplete point cloud of an object, to obtain a complete surface representation. SDS-Complete can complete a variety of objects at test time optimization without the need for an expensive collection of 3D information. We evaluate SDS-Complete on incomplete scanned objects, captured by real-world depth sensors and LiDAR scanners, and demonstrate that is effective in handling objects which are typically absent from common datasets.

Visual communication, dating back to prehistoric cave paintings, is the use of visual elements to convey ideas and information. In today's visually saturated world, effective design demands an understanding of graphic design principles, visual storytelling, human psychology, and the ability to distill complex information into clear visuals. This dissertation explores how recent advancements in vision-language models (VLMs) can be leveraged to automate the creation of effective visual communication designs. Although generative models have made great progress in generating images from text, they still struggle to simplify complex ideas into clear, abstract visuals and are constrained by pixel-based outputs, which lack flexibility for many design tasks. To address these challenges, we constrain the models' operational space and introduce task-specific regularizations. We explore various aspects of visual communication, namely, sketches and visual abstraction, typography, animation, and visual inspiration.

Point cloud data collected in real-world applications are often incomplete. This is because they are observed from partial viewpoints, which capture only a specific perspective or angle, or due to occlusion and low resolution. Existing completion approaches rely on datasets of specific predefined objects to guide the completion of incomplete, and possibly noisy, point clouds. However, these approaches perform poorly with Out-Of-Distribution (OOD) objects, which are either absent from the dataset or poorly represented. In recent years, the field of text-guided image generation has made significant progress, leading to major breakthroughs in text guided shape generation. We describe an approach called SDS-Complete that uses a pre-trained text-to-image diffusion model and leverages the text semantic of a given incomplete point cloud of an object, to obtain a complete surface representation.