Vector Field Synthesis with Sparse Streamlines Using Diffusion Model

TL;DR

This paper introduces a diffusion model framework for generating 2D vector fields from sparse streamlines, ensuring physical plausibility and fidelity to inputs.

Contribution

It proposes a novel diffusion-based approach that outperforms traditional methods in synthesizing physically consistent vector fields from limited data.

Findings

Successfully synthesizes plausible vector fields adhering to physical laws.

Outperforms traditional optimization-based approaches in flexibility and physical consistency.

Demonstrates effective reconstruction from sparse, coherent inputs.

Abstract

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. Experimental results demonstrate our method's ability to synthesize plausible vector fields that adhere to physical laws while maintaining fidelity to sparse input observations, outperforming traditional optimization-based approaches in terms of flexibility and physical consistency.