Zhen Huang

Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model, is presented and the feasibility of real-time spatial video generation for both Vidu S2-Avatar and Vidu S2-Editing is explored.

Achieving 2.8x compression in on-device speech tokenizers without sacrificing accuracy could revolutionize mobile AI applications.

Tailoring pretraining data processing to individual examples can lead to significant performance improvements in LLMs while cutting down on compute costs.

Voice-controlled video generation just got a major upgrade with Vidu S1, achieving real-time performance without visual distortion.