<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Physics-Aware Simulation | Yingqi Jie</title><link>https://initialmoon.github.io/tags/physics-aware-simulation/</link><atom:link href="https://initialmoon.github.io/tags/physics-aware-simulation/index.xml" rel="self" type="application/rss+xml"/><description>Physics-Aware Simulation</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 28 Oct 2024 00:00:00 +0000</lastBuildDate><image><url>https://initialmoon.github.io/media/icon_hu_da05098ef60dc2e7.png</url><title>Physics-Aware Simulation</title><link>https://initialmoon.github.io/tags/physics-aware-simulation/</link></image><item><title>ANFluid: Animate Natural Fluid Photos base on Physics-Aware Simulation and Dual-Flow Texture Learning</title><link>https://initialmoon.github.io/publications/anfluid/</link><pubDate>Mon, 28 Oct 2024 00:00:00 +0000</pubDate><guid>https://initialmoon.github.io/publications/anfluid/</guid><description>&lt;p&gt;ANFluid focuses on animating natural fluid photos from a single static input. It combines a physics-aware simulation module for physically plausible motion with dual-flow texture learning for improved texture prediction and content alignment.&lt;/p&gt;
&lt;p&gt;Citation: Xiangcheng Zhai, Yingqi Jie, Xueguang Xie, Aimin Hao, Na Jiang, and Yang Gao. 2024. ANFluid: Animate Natural Fluid Photos base on Physics-Aware Simulation and Dual-Flow Texture Learning. In Proceedings of the 32nd ACM International Conference on Multimedia (MM &amp;lsquo;24). Association for Computing Machinery, New York, NY, USA, 3323–3331.&lt;/p&gt;</description></item></channel></rss>