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Proposal (Summary)

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Challenge: Hard to achieve both physical accuracy and visual fidelity in digital twins.
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Existing methods:
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Our idea:
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Work with;

PhaseBand-GS: Supplement Equations [pdf]

Equations.pdf
160.7 KB
Real-Time Digital Twins with Material-Aware Gaussian Splatting

Abstract

The primary contribution of this work is the first end-to-end framework for optimizing internal physics controls directly from image-space loss.
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Beyond Initial State Optimization: We move from optimizing what a simulation starts with to optimizing how it behaves throughout its entire duration.
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Appearance-Driven Physics Control: We demonstrate a powerful new paradigm where a high-level artistic goal (a target image) can be used to derive low-level physical controls (dFc) automatically

Technical Pipeline

Summary:
Our framework establishes a seamless, fully differentiable loop between a C++ physics core and a Python-based rendering and optimization frontend.
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Forward Physics Pass (C++):
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State Transfer (C++ → Python):
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Surface synthesis (From Sparse Volemetric Physics Sim to Dense Surface Physics Sim)
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Render with 3DGS and make the pipeline differentiable
Gaussian Morphing Animation

Isaac Sim–generated datasets meet an MLS‑MPM/CPIC cutting engine for physically grounded learning.

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Current result:
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Mesh → SDF Pipeline
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Colliders (Knife/Board)
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Elasticity/Plasticity
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MPM Kernels
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Rendering/Overlay
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CLI / Output
SDF Demos:
Demo video (MLS-MPM/CPIC for the Robot Cutting Sim) :
Learning to Cut: High‑Fidelity Simulation & Dataset for Robotic Cutting

Describe Lava’s localization with wax

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Use Numeric Animation for Real Data in Gen AI
Lava Localization Simulation Based on the Wax

TVCG Teaser Video:

Rendering results:
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Sphere to Bunny & Duck to Cow:
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D to Dragon:
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TVCG:
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Abstract :
A Differentiable Material Point Method Framework for Shape Morphing
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Abstract :
Rendering results:
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Sand particle example:
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Snow with Car:
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Primitive Rectangle (vs MPM):
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Snowflakes (Neo-Hookean):
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Mater’s Thesis: (Dissertation Defense: 11/17)
MPM-Based Angular Animation of Particles using Polar Decomposition Theory