AURA: Always-On Understanding and Real-Time Assistance via Video Streams
Field: Computer Vision Authors: Xudong Lu, Yang Bo, Jinpeng Chen
Overview
Video Large Language Models (VideoLLMs) have achieved strong performance on many video understanding tasks, but most existing systems remain offline and are not well-suited for live video streams that require continuous observation and timely response.Recent streaming VideoLLMs have made progress, yet current approaches often rely on decoupled trigger-response pipelines or are limited to captioning-style narration, reducing their effectiveness for open-ended question answering and long-horizon interaction.
Contribution
We propose AURA (Always-On Understanding and Real-Time Assistance), an end-to-end streaming visual interaction framework that enables a unified VideoLLM to continuously process video streams and support both real-time question answering and proactive responses.AURA integrates context management, data construction, training objectives, and deployment optimization for stable long-horizon streaming interaction.
Results
- State-of-the-art performance on streaming video benchmarks
- Real-time demo system with ASR and TTS running at 2 FPS on two 80GB accelerators
- AURA model and real-time inference framework released to facilitate future research