Tan Guan Yuan

I am a 2nd-year PhD candidate at Monash University, focusing on 4D reconstruction and physics simulation. My goal is to build intelligent systems that perceive physical properties and causal rules from visual data, paving the way for cognitive digital twins.

I am currently exploring research internship and visiting researcher opportunities for 2026. I welcome collaborations on 3D/4D vision, neural rendering, and physics-informed learning.

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Selected Publications

Introduces a dual-network architecture that decouples local fine-grained deformation from global motion dynamics using optical flow guidance. This approach resolves the conflict between detail preservation and temporal coherence in dynamic scene reconstruction.

A framework that factorizes complex non-rigid motion into a learnable, spatially-varying basis set. This disentanglement of geometry and motion enables real-time rendering (>160 FPS) and extremely fast training (<18 mins) while maintaining high fidelity.

A mixture-of-experts framework that orchestrates open-source LLMs to generate executable Python code for complex visual tasks. Herald achieves SOTA accuracy on various benchmarks without any model training or fine-tuning.

Proposes a self-attention deep temporal convolutional network (SADTCN) to predict non-linear tremor signals, enabling accurate, real-time active suppression for Parkinson's patients.

Teaching

• FIT5215: Deep Learning (Masters)
• FIT3181: Deep Learning

Design: Jon Barron.