Yun Pang
Logo Sun Yat-sen University
I am a master's student in Computer Science and Technology at Sun Yat-sen University, supervised by Professor Guanbin Li(李冠彬). My research interests center on diffusion-based image and video generation, with an emphasis on video restoration and translation (e.g., Sim-to-Real transfer and weather translation). During my undergraduate studies, I worked with Associate Professor Xudong Mao(毛旭东), which resulted in one peer-reviewed publication and an invention patent.

Education
  • Sun Yat-sen University
    Sun Yat-sen University
    M.S. in Computer Science and Technology
    Sep. 2026 - present
  • Sun Yat-sen University
    Sun Yat-sen University
    B.S. in Artificial Intelligence
    Sep. 2022 - Jun. 2026
Honors & Awards
  • National Scholarship
    2025
  • First-Class Academic Scholarship, Sun Yat-sen University
    2025
  • Excellent Communist Youth League Member, Sun Yat-sen University
    2024
  • Second-Class Academic Scholarship, Sun Yat-sen University
    (2024、2023)
  • H Award, MCM
    2024
News
2026
Released our new preprint RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models on arXiv.
Aug 03
Released our new preprint RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos on arXiv.
Jun 14
2024
Our paper, CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization, co-authored as the second co-first author, has been accepted at AAAI 2025! check it
Dec 10
Our patent, A Personalized Image Generation Method Based on Text Inversion for Background Embedding and Feature Separation, has been officially granted!
Oct 11
Selected Publications (view all )
RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models
RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models

Yuwei Ning, Liangzhi Wang, Yi Xiao, Zhenhua Wu, Yun Pang, Mingkun Chang, Jichang Li, Guanbin Li# (# corresponding author)

Preprint 2026 Under Review

RealWeather is a driving world model for realistic and scene-faithful bidirectional weather translation. It learns authentic weather dynamics directly from real-world videos via Progressive Realism Bootstrapping, and enforces structural integrity with Scene-Fidelity RL Optimization.

RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models

Yuwei Ning, Liangzhi Wang, Yi Xiao, Zhenhua Wu, Yun Pang, Mingkun Chang, Jichang Li, Guanbin Li# (# corresponding author)

Preprint 2026 Under Review

RealWeather is a driving world model for realistic and scene-faithful bidirectional weather translation. It learns authentic weather dynamics directly from real-world videos via Progressive Realism Bootstrapping, and enforces structural integrity with Scene-Fidelity RL Optimization.

RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos
RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos

Zhenhua Wu*, Yun Pang*, Mingkun Chang*, Yuwei Ning, Liangzhi Wang, Yi Xiao, Guanbin Li# (* equal contribution, # corresponding author)

Preprint 2026 Under Review

RealityBridge is a structure-preserving and asset-aware Sim-to-Real framework for edited 3DGS driving videos. It uses multimodal controls with a lightweight GateNet for adaptive condition allocation, combined with autoregressive training and reward-guided post-training.

RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos

Zhenhua Wu*, Yun Pang*, Mingkun Chang*, Yuwei Ning, Liangzhi Wang, Yi Xiao, Guanbin Li# (* equal contribution, # corresponding author)

Preprint 2026 Under Review

RealityBridge is a structure-preserving and asset-aware Sim-to-Real framework for edited 3DGS driving videos. It uses multimodal controls with a lightweight GateNet for adaptive condition allocation, combined with autoregressive training and reward-guided post-training.

CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization
CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization

Feize Wu*, Yun Pang*, Junyi Zhang*, Lianyu Pang*, Jian Yin, Baoquan Zhao, Qing Li, Xudong Mao# (* equal contribution, # corresponding author)

The Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2025) 2025 Poster

Context Regularization (CoRe) is introduced, which enhances the learning of the new concept's text embedding by regularizing its context tokens in the prompt, thus improving the generalization of the learned text embedding.

CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization

Feize Wu*, Yun Pang*, Junyi Zhang*, Lianyu Pang*, Jian Yin, Baoquan Zhao, Qing Li, Xudong Mao# (* equal contribution, # corresponding author)

The Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2025) 2025 Poster

Context Regularization (CoRe) is introduced, which enhances the learning of the new concept's text embedding by regularizing its context tokens in the prompt, thus improving the generalization of the learned text embedding.

All publications