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Overview You will train your own flow matching model on MNIST. Part 1: Training a Single-Step Denoising UNet Unconditioned UNet architecture Visualization of the noising process Training loss curve Sample results on the test set after the first and the 5-th epoch Sample results on the test set with out-of-distribution noise levels after the model is trained.

Part 0: Project overview In this project, I implemented some fundamental functions and features of a physically-based renderer using path tracing algorithm. First, I implemented ray generation and the intersection between ray and objects/scene.

Part 0: Project overview This homework focuses on implementing fundamental operations related to Bezier curves, Bezier patches, mesh manipulation, and Loop subdivision. In this project, we started out from the basic Bezier curve and de Casteljau algorithm, and extend it to 2D surfaces.

Summary page for CS 184/284A Spring 2025 Course Projects