arXiv:2609.00901v1 Announce Type: new
Abstract: Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely...
By Hala Djeghim, Nathan Piasco, Luis Rold\~ao, Moussab Bennehar, Dzmitry Tsishkou, C\'eline Loscos, D\'esir\'e Sidib\'e
arXiv:2607. 01983v1 Announce Type: cross Abstract: Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving.
By Shuyao Li, Chuanxing Geng, Heyang Sun, Qiang Zhou, Jingjing Gu
Diffusion models have shown strong potential for multi-modal planning in end-to-end autonomous driving. However, most existing methods confine diffusion to the planning module, conditioning on fixed outputs from separate discriminative perception networks.
arXiv:2609.36810v1 Announce Type: new
Abstract: Video world models aim to predict future content from an observed scene while following prescribed camera motion. Real-world scene evolution is determi...
By Renlong Wu, Guanqiao Wang, Xuan Shang, Yin Hanming, Xiaoxiao Sheng, Tianyu Huang, Hui Li, Wangmeng Zuo
arXiv:2606. 29020v1 Announce Type: cross Abstract: Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion.
By Chenghao Qian, Nedko Savov, Lingdong Kong, Yeying Jin, Rui Song, Wenjing Li, Zhun Zhong, Jiaqi Ma, Gustav Markkula, Luc Van Gool
Bernini proposes a unified framework that separates semantic planning and pixel rendering for video generation and editing. An MLLM-based planner predicts target semantics in ViT embedding space, while a DiT-based renderer synthesizes pixels conditioned on this plan, text features, and source VAE features for editing. The approach introduces Segment-Aware 3D Rotary Positional Embedding and chain-of-thought reasoning, achieving state‑of‑the‑art performance on diverse video benchmarks.
By Bernini Team, Chenchen Liu, Junyi Chen, Lei Li, Lu Chi, Mingzhen Sun, Zhuoying Li, Yi Fu, Ruoyu Guo, Yiheng Wu, Ge Bai, Zehuan Yuan