<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>physical simulation | Yifei Li</title><link>https://liyifei.org/tag/physical-simulation/</link><atom:link href="https://liyifei.org/tag/physical-simulation/index.xml" rel="self" type="application/rss+xml"/><description>physical simulation</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© 2026 Yifei Li | 君子不器</copyright><lastBuildDate>Wed, 25 Sep 2024 00:00:00 +0000</lastBuildDate><image><url>https://liyifei.org/media/icon_huce83f9d2cff91faab74beee6f515005c_132226_512x512_fill_lanczos_center_3.png</url><title>physical simulation</title><link>https://liyifei.org/tag/physical-simulation/</link></image><item><title>NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation</title><link>https://liyifei.org/publication/neuralfluid/</link><pubDate>Wed, 25 Sep 2024 00:00:00 +0000</pubDate><guid>https://liyifei.org/publication/neuralfluid/</guid><description>&lt;h3 id="citation">Citation&lt;/h3>
&lt;pre>&lt;code>@misc{li2024neuralfluidicdesigncontrol,
title={Neural Fluidic System Design and Control with Differentiable Simulation},
author={Yifei Li and Yuchen Sun and Pingchuan Ma and Eftychios Sifakis and Tao Du and Bo Zhu and Wojciech Matusik},
year={2024},
eprint={2405.14903},
archivePrefix={arXiv},
primaryClass={physics.flu-dyn},
url={https://arxiv.org/abs/2405.14903},
&lt;/code>&lt;/pre>
&lt;p>}&lt;/p>
&lt;!-- ### Acknowledgement
We would like to thank FAIR Interfaces, FAIR X, and other members of Meta who helped in the building and release of the demo -->
&lt;h3 id="keywords">Keywords&lt;/h3>
&lt;p>fluid simulation, fluid control, navier-stokes, differentiable simulation, physical simulation, neural fluid, neural control, graphics&lt;/p></description></item><item><title>DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation</title><link>https://liyifei.org/publication/diffavatar/</link><pubDate>Fri, 01 Dec 2023 00:00:00 +0000</pubDate><guid>https://liyifei.org/publication/diffavatar/</guid><description>&lt;h3 id="pipeline">Pipeline&lt;/h3>
&lt;p>
&lt;figure >
&lt;div class="d-flex justify-content-center">
&lt;div class="w-100" >&lt;img src="pipeline.jpg#paper_image" alt="" loading="lazy" data-zoomable />&lt;/div>
&lt;/div>&lt;/figure>
&lt;/p>
&lt;p>DiffAvatar generates simulation-ready avatar assets from inputs obtained through a multi-view capture. Our pipeline initially
preprocesses the 3D scan to segment the target garment and establish the initial pose and shape of the parametric body model. We employ a
differentiable simulation framework to align our simulated garment with the segmented garment by jointly optimizing the garment’s design
and material parameters&lt;/p>
&lt;h3 id="garment-2d-representation">Garment 2D Representation&lt;/h3>
&lt;!-- ![](2d3d.png#paper_image) -->
&lt;img src=2d3d.png width="80%"/>
&lt;p>Garments can take on a wide range of 3D shapes when draped onto a body, due to factors such as changing pose and dynamics or wearer manipulations. Despite this large variation in configurations, garments are compactly represented by their 2D patterns (Fig. 3), which consist of the individual pieces of fabric that are sewn together to create the 3D clothing. Therefore, we represent clothing in 2D pattern space, which ensures developable meshes and manufacturable clothing. Virtual garments are modeled as triangle meshes, with their rest shape encoded in these 2D patterns. The rest shape is crucial for modeling the in-plane stretching and shearing behavior of different fabrics.&lt;/p>
&lt;h3 id="control-cage-garment-shape-optimization">Control Cage Garment Shape Optimization&lt;/h3>
&lt;p>We propose a regularized differentiable cage formulation to effectively and robustly optimize for the 2D patterns of garments such that the simulated and draped 3D representation of the garment closely aligns with the scan.&lt;/p>
&lt;p>&lt;strong>Control Cage Pattern Representation&lt;/strong>:
&lt;img src=control-cage.png width="20%"/>&lt;/p>
&lt;p>While it is possible to directly optimize for the 2D pattern vertices p directly, this approach is highly non-regularized and can produce ill-shaped or even non-physical inverted rest shape geometries that cause simulators to fail.&lt;/p>
&lt;p>A high number of optimization variables can also cause the optimization to get stuck in a local minimum (See our ablation study in Sec. 5.4). Additionally, directly optimizing for the 2D coordinates does not respect design constraints that are better represented in a limited subspace of reasonable designs. Therefore, we further regulate the optimization problem by selecting and optimizing a set of 2D control vertices ζ on the boundaries of the individual panels of the 2D pattern that directly deform and manipulate the underlying 2D patterns through control cages instead.&lt;/p>
&lt;p>&lt;strong>Control Cage Handle Selection&lt;/strong>: We use the geometric information of the 2D garment patterns to automatically identify control cage points, see the inset figure above. Our algorithm first extracts the boundary loop of the underlying mesh for each connected component representing a garment panel in the 2D garment pattern, then processes the boundary loop and marks a vertex as a control point if it lies on the convex hull of the pattern or when its local curvature exceeds a threshold (10° in our implementation).&lt;/p>
&lt;h3 id="demo">Demo&lt;/h3>
&lt;div class="video-container">
&lt;iframe src="https://www.youtube.com/embed/vH2MCXneAUE" class="video" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="">&lt;/iframe>
&lt;/div>
&lt;!--
### Code 💻
Follow our Github repository [Animated Drawings](https://github.com/facebookresearch/AnimatedDrawings) &lt;iframe src="https://ghbtns.com/github-btn.html?user=facebookresearch&amp;amp;repo=AnimatedDrawings&amp;amp;type=star&amp;amp;count=true&amp;amp;size=large" frameborder="0" scrolling="0" width="170" height="30" title="GitHub">&lt;/iframe>
&lt;a href="https://github.com/facebookresearch/AnimatedDrawings" target="_blank" rel="noopener">
&lt;div class="d-flex justify-content-center">
&lt;div class="w-100">&lt;img src="https://gh-card.dev/repos/facebookresearch/AnimatedDrawings.svg?fullname=" alt="facebookresearch/AnimatedDrawings - GitHub" loading="lazy" data-zoomable="" class="medium-zoom-image">&lt;/div>
&lt;/div>&lt;/a>
### Technical Posts
* Dataset of 180,000 annotated drawings: [A new, unique AI dataset for animating amateur drawings](https://ai.facebook.com/blog/ai-dataset-animating-kids-drawings/)
* Meta AI Technical post: [Using AI to bring children’s drawings to life](https://ai.facebook.com/blog/using-ai-to-bring-childrens-drawings-to-life/)
* Meta AI interview: [Meet the researcher using AI to build children’s animations](https://tech.facebook.com/artificial-intelligence/2021/12/ai-childrens-drawings/)
### Dataset
We have open sourced our [dataset](https://github.com/facebookresearch/AnimatedDrawings#amateur-drawings-dataset) of ~180,000 annotated drawings, which you can explore for your own research.
### Website
Go to [here](https://fairanimateddrawings.com/site/home)
### Video
&lt;div class="video-container">
&lt;iframe src="https://www.youtube.com/embed/WsMUKQLVsOI" class="video" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="">&lt;/iframe>
&lt;/div>
&lt;div class="video-container">
-->
&lt;h3 id="citation">Citation&lt;/h3>
&lt;pre>&lt;code>@inproceedings{li2023diffavatar,
title={{DiffAvatar}: Simulation-Ready Garment Optimization with Differentiable Simulation},
author={Li, Yifei and Chen, Hsiao-yu and Larionov, Egor and Sarafianos, Nikolaos and Matusik, Wojciech and Stuyck, Tuur},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
eprint={2311.12194},
year = {2024}
}
&lt;/code>&lt;/pre>
&lt;!-- ### Acknowledgement
We would like to thank FAIR Interfaces, FAIR X, and other members of Meta who helped in the building and release of the demo -->
&lt;h3 id="keywords">Keywords&lt;/h3>
&lt;p>graphics, physical simulation, digital avatar, differentiable simulation, cloth simulation&lt;/p></description></item></channel></rss>