Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Currently learning Position Based Dynamics. Soft Body Simulation is fun!

16,868 Aufrufe • vor 1 Jahr •via X (Twitter)

10 Kommentare

Profilbild von Marius Fabry 🍣
Marius Fabry 🍣vor 1 Jahr

Did some experiments with this a long time ago, but also wanted it to be multiplayer over network, which proved difficult 😂

Profilbild von PowerBeatsVR
PowerBeatsVRvor 3 Jahren

VR fitness app PowerBeatsVR has just made its way to the official Meta Quest store! Grab it now and enjoy a fun workout anywhere, anytime, and without any subscription ever:

Profilbild von SpyreIO
SpyreIOvor 1 Jahr

Looks awesome! I've done a ton of rigid body kinematics, but never soft body. Level with me: is it difficult? What solver are you using?

Profilbild von matsuoka-601
matsuoka-601vor 1 Jahr

This solver is the one I've been implementing for about 2 weeks. The core idea of this simulation is pretty simple — Position Based Dynamics (PBD) with distance and volume constraint (see section 4.3 in the original PBD paper), which you can implement <200 LOC I guess.

Profilbild von Iwo Plaza | TypeGPU
Iwo Plaza | TypeGPUvor 1 Jahr

Looks so good that it’s making me hungry 😭🥝🍋‍🟩🍏

Profilbild von Javier Meseguer
Javier Meseguervor 1 Jahr

Looks great!

Profilbild von Surf Da Earf
Surf Da Earfvor 1 Jahr

so sic, softbodys are sweet

Profilbild von GoPal
GoPalvor 1 Jahr

what are you using to visualise it

Profilbild von matsuoka-601
matsuoka-601vor 1 Jahr

I'm using three.js. Each blob is just a collection of triangles, so drawing it is pretty easy.

Profilbild von Terence Watson
Terence Watsonvor 1 Jahr

my son would love this. i need to do this too

Ähnliche Videos

Introducing ✨RigidFormer: Learning Rigid Dynamics with Transformers - our attempt to scale learning-based physical dynamics with Transformers. RigidFormer learns rigid dynamics with Transformers. It is a mesh-free, object-centric Transformer for multi-object rigid-body contact dynamics from point clouds. Learning physics with purely neural simulators, without relying on traditional physics engines, is an important and widely studied problem. Prior SOTA methods often use graph neural networks for accuracy and generalization, but still struggle with efficient, high-fidelity simulation at scale. RigidFormer uses only point inputs, matches or outperforms mesh-based baselines on standard benchmarks, runs much faster, generalizes across point resolutions and datasets, and scales to 200+ objects. We also show a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components. RigidFormer is mesh-free: it does not require mesh connectivity, SDFs, or vertex-level message passing, making it well-suited for point-cloud observations and scalable simulation. This architecture can also be adapted to learn soft-body dynamics by replacing the rigid-body module (differentiable Kabsch alignment). 🎬See our video for more details. Many thanks to my amazing collaborators: Minghao Guo Minghao Guo, Haixu Wu Haixu Wu 吴海旭, Doug Roble, Tuur Stuyck Tuur Stuyck, and Wojciech Matusik Wojciech Matusik. Project page: Paper:

Zhiyang (Frank) Dou

571,242 Aufrufe • vor 1 Monat