5 citations · 11 across the 5 of their papers we have counts for
5 papers
ElastoGen: 4D Generative Elastodynamics
Yutao Feng, Yintong Shang, Xiang Feng +7
We present ElastoGen, a knowledge-driven AI model that generates physically accurate 4D elastodynamics. Unlike deep models that learn from video- or image-based observations, Elast…
VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality
Ying Jiang, Chang Yu, Tianyi Xie +8
As consumer Virtual Reality (VR) and Mixed Reality (MR) technologies gain momentum, there's a growing focus on the development of engagements with 3D virtual content. Unfortunately…
Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering
Yutao Feng, Xiang Feng, Yintong Shang +8
We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructe…
PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
Yutao Feng, Yintong Shang, Xuan Li +3
We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. Unlike existing methods, we discretize…
PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics
Tianyi Xie, Zeshun Zong, Yuxing Qiu +4
We introduce PhysGaussian, a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employ…