3D Computer Graphics Foundations for GANs in PyTorch

Generative AI is rapidly expanding from flat 2D images to immersive 3D worlds, but mastering these advanced models requires a solid grasp of traditional computer graphics. Understanding how 3D assets are represented, transformed, and projected is essential for any developer looking to work with modern generative frameworks. This text-only course bridges the gap between classic computer graphics and modern machine learning. You will build a conceptual and mathematical foundation in 3D space, coordinate systems, and rendering pipelines, enabling you to confidently understand and write PyTorch code for 3D-aware Generative Adversarial Networks (GANs). What you'll learn: - Understand core 3D representations including meshes, voxels, point clouds, and neural radiance fields. - Apply coordinate space transformations, projection matrices, and camera models to position 3D objects. - Configure rendering pipelines conceptually to map 3D scenes onto 2D image planes. - Analyze how Generative Adversarial Networks leverage 3D representations to synthesize realistic multi-view images. - Practice writing clean PyTorch code to manipulate 3D spatial data and coordinate systems. We begin with key terminology and the fundamental mathematics of 3D space before exploring camera projections, rendering concepts, and finally integrating these principles into modern generative deep learning architectures. This course is designed for beginners eager to enter the field of 3D deep learning, requiring no prior computer graphics experience. Start reading today to unlock the power of 3D computer graphics in your machine learning workflows.