Vector Field Synthesis with Sparse Streamlines Using Diffusion Model

ArXi:2604.09838v1 Announce Type: new We present a novel diffusion-based framework for synthesizing 2D vector fields from sparse, coherent inputs (i.e., streamlines) while maintaining physical plausibility. Our method employs a conditional denoising diffusion probabilistic model with classifier-free guidance, enabling progressive reconstruction that preserves both geometric and physical constraints.