DeepMind Blog

Veo 3.1 Ingredients to Video: More consistency, creativity and control

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Our latest Veo update generates lively, dynamic clips that feel natural and engaging — and supports vertical video generation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at DeepMind Blog.

arXiv Machine Learning
Sep 11

Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

Vidu S2 is a system that includes Vidu S2-Avatar, a real‑time interactive digital‑character model, and Vidu S2-Editing, a real‑time video editing model. It enables real‑time 720p video generation with dynamic references and improved instruction following, such as dancing, and allows real‑time editing of video streams for style rendering, clothing replacement, character replacement, and background replacement. Experiments show Vidu S2 outperforms all baselines, and a playable online demo is available at https://vidu.com/vidu-stream.

By Jintao Zhang, Kai Jiang, Jintao Chen, Xu Wang, Deyuan Liu, Jungang Li, Dechuang Chen, Ming Lin, Jingjiang Zhou, Haopeng Jin, Qi Jia, Xiaohang Wang, Yaole Wang, Zhanqiang Zhang, Ran Li, Zhengkun Huang, Shuyue Xiong, Yuji Wang, Zikun Dai, Hui He, Yang Luo, Mang Ning, Weiqi Feng, Chengyang Ye, Xinyue Lin, Min Zhao, Hongzhou Zhu, Hengkai Tan, Zeyuan Wang, Chendong Xiang, Kaiwen Zheng, Zhijie Deng, Fan Bao, Jianfei Chen, Jun Zhu
arXiv Computer Vision
Sep 10

Visko Orbis 1.0: A Live Model for Real-Time Interactive Long Video Generation

arXiv:2607.26694v3 Announce Type: replace Abstract: We present Visko Orbis 1.0, a Live Model for real-time, interactive long video generation. Users can change the prompt at any moment during generat...

By Xiangbo Gao, Siyuan Yang, Ping He, Mingyang Wu, Yuheng Wu, Yushen Zuo, Jiongze Yu, Ryan Cui, Hongyuan Hua, Devin Ma, Xiao Jin, Yubo Ruan, Qing Yin, Jie Yang, Zhengzhong Tu
arXiv Computer Vision
Sep 7

TokenDial: Continuous Attribute Control for Text-to-Video Generation in Visual Dial Space

TokenDial introduces a Visual Dial Space (V+) where the channel dimension of visual patch tokens in video diffusion transformers acts as a semantic control space. By learning additive directions in V+, the framework enables continuous slider-style edits for appearance and motion attributes without altering the pretrained generator. The method demonstrates improved controllability and content preservation compared to prior video editing techniques.

By Zhixuan Liu, Peter Schaldenbrand, Yijun Li, Long Mai, Aniruddha Mahapatra, Cusuh Ham, Jean Oh, Jui-Hsien Wang