# HybridSim: A Physics-Learning Hybrid Digital Twin for mmWave Human Sensing

arXiv: https://arxiv.org/abs/2607.15806 · HTML: https://mlsanity.com/paper/2607.15806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [HUGS: Human Gaussian Splats](https://mlsanity.com/paper/2311.17910/) (2023)
- [NeRF2: Neural Radio-Frequency Radiance Fields](https://mlsanity.com/paper/2305.06118/) (2023)
- [RF-3DGS: Wireless Channel Modeling with Radio Radiance Field and 3D Gaussian Splatting](https://mlsanity.com/paper/2411.19420/) (2024)
- [GSRF: Complex-Valued 3D Gaussian Splatting for Efficient Radio-Frequency Data Synthesis](https://mlsanity.com/paper/2502.01826/) (2025)
- [Radio Frequency Ray Tracing with Neural Object Representation](https://mlsanity.com/paper/2411.18635/) (2024)
- [InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin](https://mlsanity.com/paper/2508.14204/) (2025)
- [Differentiable Radio Frequency Ray Tracing for Millimeter-Wave Sensing](https://mlsanity.com/paper/2311.13182/) (2023)

## Abstract (arXiv)

High-fidelity simulation of mmWave radar signals for dynamic human motion is valuable for developing radar-based human sensing models; yet collecting accurately labeled measurements for a specific deployment site remains expensive. We present HybridSim, a physics-learning hybrid simulator that synthesizes mmWave radar signals from dynamic human meshes under a fixed indoor room configuration, explicitly decoupling propagation into two components. To parameterize the human subject, we use a tri-plane representation to extract human features and a Graph Convolutional Network to stabilize optimization and mitigate gradient instability. The direct signal path is modeled via an inverse-rendering formulation with a microfacet BRDF to capture primary surface reflections. In parallel, the indirect path is approximated by combining 3D Gaussian Splatting with a virtual-receiver geometry to fit and reproduce site-specific multipath interference patterns, achieving substantially lower computational cost than explicit full ray tracing. Experiments in a fixed-room setting show improved agreement with a physically based reference and consistent gains on downstream radar-based human sensing tasks when using HybridSim for site-specific data augmentation.