用神经元胞自动机实现实时烟雾风格化，保持时空一致
Volumetric Temporal Texture Synthesis for Smoke Stylization using Neural Cellular Automata
- 基于欧拉框架的神经元胞自动机，自动生成3D风格纹理
- 训练时间减少超10倍，实现烟雾帧间动态对齐与连贯过渡
- 适用于烟雾与网格风格化，适合实时图形生成应用
由于难以在合理时间和计算资源下确保参考风格图像的时空一致性，3D体积烟雾的艺术风格化仍是计算机图形学中的挑战。本文提出体积神经元胞自动机（VNCA），一种高效体积风格迁移新模型，可实时生成多视角一致的风格化特征，并实现风格化模拟帧间的时序连贯过渡。VNCA在欧拉框架下合成包含颜色与密度风格化的3D纹理体，并动态对齐其与烟雾模拟的复杂运动模式。该方法用自涌现的元胞自动机运动替代显式的流体输运建模和帧间平滑项，使训练时间减少超过一个数量级。除烟雾模拟外，我们还展示了该方法在网格风格化上的通用性。
原文摘要 · Abstract (English)
Artistic stylization of 3D volumetric smoke data is still a challenge in computer graphics due to the difficulty of ensuring spatiotemporal consistency given a reference style image, and that within reasonable time and computational resources. In this work, we introduce Volumetric Neural Cellular Automata (VNCA), a novel model for efficient volumetric style transfer that synthesizes, in real-time, multi-view consistent stylizing features on the target smoke with temporally coherent transitions between stylized simulation frames. VNCA synthesizes a 3D texture volume with color and density stylization and dynamically aligns this volume with the intricate motion patterns of the smoke simulation under the Eulerian framework. Our approach replaces the explicit fluid advection modeling and the inter-frame smoothing terms with the self-emerging motion of the underlying cellular automaton, thus reducing the training time by over an order of magnitude. Beyond smoke simulations, we demonstrate the versatility of our approach by showcasing its applicability to mesh stylization.
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