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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 a staggering 54.67× speedup in text-to-video-audio generation without sacrificing quality could revolutionize real-time multimedia applications.
Voice-controlled video generation just got a major upgrade with Vidu S1, achieving real-time performance without visual distortion.
Teacher-forcing consistency models can accelerate autoregressive video generation by ten times, revolutionizing the training landscape for streaming applications.
Making interactive video world models real-time just got easier: minWM offers a full-stack, open-source pipeline to convert existing diffusion models into controllable, low-latency generators.
Forget synthetic data that looks like it came from a PS2 game: NVIDIA's new Cosmos-Predict2.5 generates high-fidelity videos for training embodied AI, opening the door to more realistic and reliable simulations.
