水景的欧拉运动重建
Eulerian Motion Reconstruction for Water Scenery
查看机构详情
- Carnegie Mellon University（卡内基梅隆大学）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- Meta
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文提出从3D角度重建水景的循环4D动态，利用静态欧拉运动场与残差项建模非周期动态，实现优于现有技术的照片级真实动画。
中文摘要 AI 辅助
从自然中重建并动画化水景可产生引人入胜且沉浸式的视觉体验。先前的工作从2D视频纹理的角度审视了这一任务，目标是创建循环视频。在我们的工作中，我们从3D角度解决该问题，从单个非循环2D源视频创建可交互地从新视角渲染的循环4D动态重建。我们将运动表示为3D静态欧拉运动场，该场平流输送在固定时间段周期性重生的规范高斯溅射，并使用渲染损失进行监督。为了模拟真实场景中存在的非周期性和随机动态，我们添加了一个非周期性、时变的残差项来捕获对静态欧拉运动场的偏差。我们定量和定性地表明，我们的框架能够比现有技术更好地实现水景的照片级真实动画。
英文摘要
Reconstructing and animating water scenery from nature produces compelling and immersive visual experiences. Previous work examined this task from the perspective of 2D video textures, with the goal of creating a looping video. In our work, we tackle the problem from a 3D perspective, creating a looping 4D dynamic reconstruction which can be interactively rendered from novel viewpoints from a single non-looping 2D source video. We represent motion as a 3D static \textit{Eulerian} motion field that advects canonical Gaussian splats that are cyclically reborn at fixed time periods, supervised using rendering losses. To model non-periodic and stochastic dynamics present in real-world scenes, we add a non-periodic, time-varying residual term to capture deviations from the static Eulerian motion field. We show quantitatively and qualitatively that our framework enables photorealistic animation of water scenes better than prior art.
