INRFlow：环境空间中INR的流匹配
Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or even protein...
流匹配模型在图像、视频及不规则数据（如3D点云和蛋白质结构）生成建模中表现优异。传统方法需分两阶段训练，限制了跨数据域的统一性。INRFlow提出了一种领域无关的方法，直接在环境空间中学习流匹配变换器，实验结果显示其在多种数据类型上表现出色，具有广泛应用潜力。
