arXiv:2606.25041v2 Announce Type: replace-cross Abstract: We present Wan-Streamer, a native-streaming, end-to-end interactive foundation model designed from the ground up for real-time, low-latency, full-duplex audio-visual interaction. Wan-Streamer seamlessly models language, audio, and video as both input and output within a single Transformer, where the sequence is represented as interleaved visual, audio, and text input tokens together with visual, audio, and text output tokens, coordinated by block-causal attention for incremental streaming. Unlike cascaded interactive systems that rely o
The continuous drive for more natural and efficient human-AI interaction is pushing the boundaries of multimodal AI development, leading to advancements like Wan-Streamer.
This development indicates significant progress towards truly interactive, real-time multimodal AI, which could redefine human-computer interfaces and autonomous systems.
The ability to seamlessly process and generate interleaved visual, audio, and text in real-time within a single model marks a departure from cascaded, latency-prone systems.
- · AI developers
- · Human-computer interaction sector
- · Robotics
- · Virtual/Augmented Reality
- · Legacy multimodal AI architectures
- · Interaction models reliant on high latency
Wan-Streamer improves the fluidity and naturalness of real-time AI interactions.
This could accelerate the development of more capable and human-like AI assistants and autonomous agents.
Widespread adoption of such interactive models might fundamentally alter how humans collaborate with AI in professional and personal contexts.
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Read at arXiv cs.AI