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Vidu S1: A Real-Time Interactive Video Generation Model

Generates real-time, infinite-length interactive videos of voice-controllable digital characters — 540p at up to 42 FPS on consumer GPUs. Uses TurboDiffusion and TurboServe to maintain temporal coherence without blur or drift, and accepts custom person, anime, or pet images plus selectable voice tones.

Introduction

Most generative video work focuses on offline short clips; this paper targets the opposite constraint: sustained, low-latency, interactive video that users can steer at any moment via voice. The core contribution is a full-stack approach that keeps frames coherent over unbounded durations while meeting real-time inference on commodity GPUs.

Key Capabilities
  • Real-time interactive control: voice instructions can change content at any frame, enabling live direction of digital characters — so what: enables livestream avatars and interactive demos rather than pre-rendered clips.
  • Infinite-length coherence: design choices and temporal handling prevent blur, drift, and visual distortion over long runs — so what: avoids the usual accumulation-of-error that breaks long video generation.
  • Practical performance: outputs 540p video at up to 42 FPS on regular consumer GPUs using TurboDiffusion (model) and TurboServe (serving stack) — so what: brings research-level interactive video into workable deployment on common hardware.
  • Customization: accepts uploaded images of real people, anime, or pets and supports selectable voice tones — so what: allows rapid personalization for avatars and branded characters.
Who it's for & tradeoffs

Great fit if you need live-interactive virtual avatars, real-time demos, or streaming integrations where latency and continuous coherence matter. Look elsewhere if your priority is high-resolution cinematic output (beyond 540p) or extremely low-resource mobile deployment; the system targets consumer GPUs rather than tiny-edge devices. Also expect typical generative-AI caveats around identity, copyright, and potential artifacts in complex motions.

Where it fits

Positioned between offline high-quality video synthesis and lightweight avatar rigs: it sacrifices ultimate perceptual fidelity for temporal stability and real-time responsiveness, making it a practical choice for interactive applications rather than film-quality generation.

Technical note

The paper pairs a TurboDiffusion model with a dedicated serving stack (TurboServe) and engineering optimizations to sustain high FPS and temporal consistency; the authors provide a playable demo to validate real-time behavior rather than only offline metrics.

Information

  • Websitearxiv.org
  • AuthorsJintao Zhang, Kai Jiang, Jintao Chen, Xu Wang, Yang Luo, Yuji Wang, Dechuang Chen, Jungang Li, Chengyang Ye, Marco Chen …
  • Published date2026/07/03

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