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Vinci: A Real-time Embodied Smart Assistant based on Egocentric Vision-Language Model

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arxiv 2412.21080 v1 pith:RIT5NFRV submitted 2024-12-30 cs.CV

Yifei Huang , Jilan Xu , Baoqi Pei , Yuping He , Guo Chen , Lijin Yang , Xinyuan Chen , Yaohui Wang
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Zheng Nie Jinyao Liu Guoshun Fan Dechen Lin Fang Fang Kunpeng Li Chang Yuan Yali Wang Yu Qiao Limin Wang
This is my paper · ORCID
classification cs.CV
keywords vincireal-timeegocentricassistanceassistantembodiedmodelportable
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce Vinci, a real-time embodied smart assistant built upon an egocentric vision-language model. Designed for deployment on portable devices such as smartphones and wearable cameras, Vinci operates in an "always on" mode, continuously observing the environment to deliver seamless interaction and assistance. Users can wake up the system and engage in natural conversations to ask questions or seek assistance, with responses delivered through audio for hands-free convenience. With its ability to process long video streams in real-time, Vinci can answer user queries about current observations and historical context while also providing task planning based on past interactions. To further enhance usability, Vinci integrates a video generation module that creates step-by-step visual demonstrations for tasks that require detailed guidance. We hope that Vinci can establish a robust framework for portable, real-time egocentric AI systems, empowering users with contextual and actionable insights. We release the complete implementation for the development of the device in conjunction with a demo web platform to test uploaded videos at https://github.com/OpenGVLab/vinci.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

    cs.CV 2026-07 conditional novelty 6.5 of 10

    EgoMemo uses multi-scale temporal summaries, a knowledge graph, and visual archives to decide whether and when to intervene proactively on continuous egocentric video, setting baselines on the new EgoServe benchmark o...

  2. EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?

    cs.CV 2025-06 conditional novelty 6.0 of 10

    EOC-Bench evaluates MLLMs on egocentric object cognition across past, present, and future temporal dimensions, finding large gaps versus humans, especially in absolute time perception.

  3. Egocentric Action-aware Inertial Localization in Point Clouds with Vision-Language Guidance

    cs.CV 2025-05 conditional novelty 6.0 of 10

    EAIL localizes a person in a 3D point cloud from head-mounted IMU signals by aligning short action segments with scene locations using vision-language training guidance.

  4. Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision

    cs.CV 2025-06 accept novelty 3.0 of 10

    A comprehensive review of cross-view video understanding that uses both first-person and third-person cameras, organized into a three-direction taxonomy with a dataset catalog and future research gaps.

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