用扩散模型从稀疏流线生成符合物理规律的二维向量场
Vector Field Synthesis with Sparse Streamlines Using Diffusion Model
- 基于条件去噪扩散模型，通过无分类器引导实现渐进式重建
- 生成结果在几何与物理约束上均优于传统优化方法
- 适合流体模拟、医学图像分析等需要物理一致性的场景
我们提出一种新型扩散框架，可从稀疏且连贯的输入（即流线）中合成二维向量场，同时保持物理合理性。方法采用带有无分类器引导的条件去噪扩散概率模型，实现逐步重建，有效保留几何与物理约束。实验表明，该方法能生成符合物理定律且忠实于稀疏输入的合理向量场，在灵活性和物理一致性上优于传统优化方法。
原文摘要 · Abstract (English)
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.
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