发表机构
Alibaba Group（阿里巴巴集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出Wan-Streamer v0.3，将视频视为世界加事件流，据此有通用预训练任务，应用于实时全双工视听交互，保留v0.2运行点，为实时下游任务提供能力。
AI 中文摘要
我们展示了Wan-Streamer v0.3，它在单一组织视角下重塑了原生流交互模型：视频是一个世界加一个事件流。世界是视频展开的持久上下文，事件流是世界中随时间变化的一切。这产生了一个针对大量真实视频的通用预训练任务。我们将其应用于实时全双工视听交互。该模型保留了v0.2的运行点，如视频分辨率、帧率等参数。
英文摘要
We present Wan-Streamer v0.3, which reframes our native-streaming interaction model under a single organizing view: a video is a world plus an event stream. The world is the persistent context in which a video unfolds, including the environment, scene, subjects, ambient acoustic conditions, voice characteristics, and other relatively stable conditions. The event stream is everything that changes over time within that world, including scene or environmental changes, subject behavior, speech, and other sounds. This yields a general-purpose pretraining task over large amounts of real video: given a world and incoming input, predict how the world moves, changes, and responds in real time. The resulting competence can be specialized to a broad family of real-time downstream tasks. We instantiate it on real-time full-duplex audio-visual interaction, where the event stream is the agent's speech together with free-form behavior. Functionally, the model's multimodal understanding process is vision-language-action-like: it maps multimodal user input to language-form speech and behavior actions. Wan-Streamer v0.3 preserves the v0.2 operating point: 640x368 video at 25 FPS, a 160 ms streaming unit, approximately 200 ms model-side response latency, and approximately 550 ms total interaction latency under a 350 ms bidirectional network budget.
Commentswebsite: https://wan-streamer.com/v0.3/
