Renhao Huang

I am the Postdoc Research Fellow and the associate director for AI research at iCinema Research Centre, UNSW, focusing on developing AI models for iFire project. In this project, I mainly explore deep learning–based approaches to wildfire behaviour modelling, bridging advanced computation with urgent real-world challenges.

In 2020, I received the B.E degree with a first-class honour from the University of New South Wales. In 2025, I received my PhD degree at the School of Computer Science and Engineering, UNSW, with my thesis Towards Robust and Lightweight Vision-based Pedestrian Trajectory Prediction.

My current research focuses on:

Human Trajectory Prediction
Computer Vision
Domain Adaptation & Generalisation
Wildfire Research

News

July, 2025	One abstract accepted by X-Fire, 1st International Workshop on Extreme Wildfire Events
March, 2025	PhD Thesis accepted.
March, 2025	One paper accepted by TNNLS.
October, 2024	Started Postdoc Research Fellow at iCinema Research Centre.
June, 2024	One paper accepted by RA-L

Selected Publications

Vision-based Multimodal trajectory prediction: A survey

Renhao Huang, Hao Xue, Maurice Pagnucco, and 2 more authors

IEEE Transactions on Neural Networks and Learning Systems, 2025

[ Bib ] [ Paper ]

@article{survey-mtp-arxiv,
  title = {Vision-based Multimodal trajectory prediction: A survey},
  author = {Huang, Renhao and Xue, Hao and Pagnucco, Maurice and Salim, Flora and Song, Yang},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  year = {2025},
}

Fully Decoupling Trajectory and Scene Encoding for Lightweight Heatmap-oriented Trajectory Prediction

Renhao Huang, Jingtao Ding, Maurice Pagnucco, and 1 more author

IEEE Robotics and Automation Letters, 2024

[ Bib ] [ Paper ]

@article{decouple-traj,
  author = {Huang, Renhao and Ding, Jingtao and Pagnucco, Maurice and Song, Yang},
  journal = {IEEE Robotics and Automation Letters},
  title = {{Fully Decoupling Trajectory and Scene Encoding for Lightweight Heatmap-oriented Trajectory Prediction}},
  year = {2024},
  pages = {9143--9150},
}

HyperTraj: Towards Simple and Fast Scene-compliant Endpoint Conditioned Trajectory Prediction

Renhao Huang, Maurice Pagnucco, and Yang Song

In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023

[ Bib ] [ Paper ]

@inproceedings{hypertraj,
  title = {{HyperTraj: Towards Simple and Fast Scene-compliant Endpoint Conditioned Trajectory Prediction}},
  author = {Huang, Renhao and Pagnucco, Maurice and Song, Yang},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year = {2023},
}

Towards Single Source Domain Generalisation in Trajectory Prediction: A Motion Prior based Approach

Renhao Huang, Anthony Tompkins, Maurice Pagnucco, and 1 more author

In Conference on Lifelong Learning Agents, 2023

[ Bib ] [ Paper ]

@inproceedings{mp-traj,
  author = {Huang, Renhao and Tompkins, Anthony and Pagnucco, Maurice and Song, Yang},
  title = {{Towards Single Source Domain Generalisation in Trajectory Prediction: A Motion Prior based Approach}},
  booktitle = {Conference on Lifelong Learning Agents},
  year = {2023},
  pages = {227--243},
}