2026
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
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.
2025
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.