JoyAI-Video-Edit: Real-Time Open-Ended Video Editing with Autoregressive Diffusion
Authors: name: Yicheng Xiao, name: Wenxun Dai, name: Xinran Qin, name: Lin Song, name: Maoquan Zhang, name: Hang Xu, name: Yukang Chen, name: Yitong Li, name: Guohui Zhang, name: Yuan Zhang, name: Xuying Zhang, name: Tommy Zhang, name: Jianlong Yuan, name: Peihao Li, name: Shuai Lu, name: Siming Fu, name: Chuyang Zhao, name: Xin Han, name: Jie Huang, name: Wenbo Li, name: Guoqing Ma, name: Wei Huang, name: Xiaojuan Qi, name: Haoyang Huang, name: Nan Duan
Organization: jingdong

Real-time video editing requires low-latency causal generation with bounded computational resources while preserving source fidelity and long-term temporal consistency. We present JoyAI-Video-Edit, a 16B-parameter autoregressive diffusion framework for real-time, open-ended video editing without access to future frames or a predefined video duration. Our method combines chunk-wise autoregressive adaptation, Source-Anchored Distribution Matching Distillation (SA-DMD), and Long-Horizon Autoregressive Distillation to reduce train--inference mismatch, preserve source fidelity during two-step generation, and mitigate accumulated temporal drift. Extensive automatic and human evaluations show that JoyAI-Video-Edit substantially outperforms existing streaming editors and remains competitive with strong offline systems on both short and long videos. The complete system achieves end-to-end 720p video editing at approximately 30 FPS on a single Nvidia B200 GPU. Code is available at https://github.com/jd-opensource/JoyAI-Video-Edit.