Distance Marching for Generative Modeling
DISTANCE MARCHING introduces a time-unconditional generative model by using distance field modeling. It reformulates generation to learn scalar fields that represent the distance to data targets, rather than relying on time conditioning. This approach yields denoising directions that are more accurately aimed toward the desired data manifold, improving generation quality.