CPSC 533R 201 2021W

Computer Graphics (CG) enables artists to realize their creative visions. Technically, one is concerned with efficiently simulating light transport for photorealistic rendering, finding the right parametrization for the shape and appearance of objects, and making these digital models accessible to the artist. Creating digital content is a tedious process, which is alleviated by Computer Vision (CV) by reconstructing real scenes from natural images.

This course will explore the recent trend of using Deep Learning (DL) for the above-mentioned tasks; neural rendering, generative networks, disentangled representation learning, and reconstruction using neural networks. The part of the course will introduce the essential tools (CNN architectures, attention models, output representations, and batch-norm layers), those needed to understand the advanced topics covered in the main part (self-supervised and generative models, such as VAEs and GANs).

A central theme is the learning of neural representations from labeled and unlabeled data. At one end, we will discuss approaches that can reconstruct a person photorealistic, from a single image and up to the scale of wrinkles, by using a volumetric grid with thousands of parameters. On the other end, we will study techniques for learning compact and meaningful embeddings, such as a parametric face model that decomposes expression, appearance, and pose and makes these latent dimensions controllable by the artist. At the end of the course, students will be able to work with a diverse spectrum of DL tools and be prepared to conduct research on how to create deep learning models that yield both, high visual quality and have artistic utility.