Hello

Thanks for reaching out, Baymurat. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

Can I integrate body language reading in livekit?

Thank you for reaching out! LiveKit offers some wonderful video processing features like face landmark detection and analyzing video streams for things like sentiment analysis and scene understanding. While it doesn’t specifically include body language reading features, I’d be happy to hear more about your specific use case. Together, we can explore how LiveKit’s current capabilities might support your needs or look into some helpful integrations. Please feel free to share more details! Sources: Live Conversations with AI using ChatGPT and WebRTC | examples/face_landmark/face_landmark.py

what is face landmark detection?

Face landmark detection is a wonderful way to identify and track key facial features—like the eyes, nose, mouth, and jawline—right within a video stream using computer vision models. In LiveKit, we showcase this through a neat integration with Google's MediaPipe framework. Here’s a friendly overview of how it works: • You subscribe to a video track in a LiveKit room and receive video frames. • Each frame is then processed using MediaPipe's face landmark model to detect those facial features. • The detected landmarks are overlaid as a mesh directly on the video frame. • Finally, the processed frame is displayed locally using OpenCV. This feature opens up exciting possibilities like real-time avatars, augmented reality effects, and facial expression analysis. If you'd like, I’m here to help with more details or guide you through implementing this—just let me know! Sources: examples/face_landmark/requirements.txt | README.md | Simli virtual avatar integration guide | LiveKit Docs

can I recognize the face smiling, so that AI agent says 'Oh I see you are smiling' something like that

Absolutely, you can create a feature where an AI agent recognizes a smile and responds warmly, like saying "Oh, I see you are smiling." While LiveKit itself doesn’t directly detect facial expressions such as smiling, it wonderfully supports integrations with tools like BitHuman and Simli that make this possible. Here’s how you might approach it: • BitHuman Integration: You can use the "expression" model to detect facial expressions in real-time and have your AI respond with dynamic emotional cues. • Simli Integration: By configuring emotions like joy (which corresponds to smiling) with the

emotion_id

parameter, your AI agents can trigger thoughtful and fitting responses. These integrations open up exciting possibilities for your AI agents to connect more naturally by recognizing and responding to facial expressions in real-time. If you'd like, I’m here to help guide you through setting this up—just let me know! Sources: Bringing AI avatars to voice agents | livekit-plugins/livekit-plugins-bithuman/livekit/plugins/bithuman/avatar.py | Simli virtual avatar integration guide | LiveKit Docs

Okay, how can I create it with livekit, is there any documentation?

I'm excited to help you create AI agents with facial expression detection and response using LiveKit integrations like BitHuman and Simli. Let’s walk through the steps together, and I’ve included some helpful documentation links to guide you: 1. BitHuman Integration: • First, install the required package with:

pip install "livekit-agents[bithuman]~=1.2"

• Then, set up your authentication using

BITHUMAN_API_SECRET

. • Make sure to use the "expression" model to get real-time emotional responses and dynamic facial expressions. • Here’s the detailed documentation to help you along: BitHuman Integration Guide 2. Simli Integration: • Start by installing the package:

pip install "livekit-agents[simli]~=1.2"

• Set up your authentication with

SIMLI_API_KEY

. • You can customize emotions, like setting "happy," using the

emotion_id

parameter. • Check out this guide for more details: Simli Integration Guide To make things even easier, here are some additional examples and resources you might find useful: • BitHuman Example • Simli Example • LiveKit Agents Playground If you have any questions or need a hand with the implementation, please don’t hesitate to reach out. I’m here to help! Sources: examples/avatar_agents/bithuman/requirements.txt | bitHuman virtual avatar integration guide | LiveKit Docs | Simli virtual avatar integration guide | LiveKit Docs

I don't visual avatars, I need a thing that detects a face expression

I'm glad you're interested in using LiveKit for real-time facial landmark detection with MediaPipe—it’s a fantastic way to analyze expressions like smiling! Here’s a simple way to get started: 1. First, subscribe to a video track in a LiveKit room to receive the video frames. 2. Next, use MediaPipe’s Face Landmarker model to extract the facial landmarks from those frames. 3. Then, you can analyze the landmark data to detect a smile by looking at things like: ◦ The horizontal width of the mouth (measuring the distance between the corners of the lips). ◦ How open the mouth is vertically. ◦ Features like eye narrowing that often accompany a smile. 1. Finally, you’d implement some custom logic or even a machine learning model to classify these measurements as a smile. While LiveKit helps you get the facial landmarks, the actual smile detection part would be something you’d build on top. If you’d like, I’m here to help guide you through setting up the landmark detection or integrating your own smile recognition logic—just let me know! Sources: examples/face_landmark/requirements.txt | src/components/LocalAvatarView/rigging.tsx

Okay, how can I use MediaPipe with livekit?

I'm glad you're interested in using MediaPipe with LiveKit for facial landmark detection! Here's a simple way to get started: 1. First, subscribe to a video track in your LiveKit room to receive the video frames. 2. Next, use the MediaPipe Face Landmarker model to process those video frames and extract the facial landmarks. 3. Then, you can analyze the landmark data for whatever you need—like detecting smiles or other expressions. I want to be upfront that there isn't specific documentation or examples available for integrating MediaPipe with LiveKit just yet. But please don’t worry—if you'd like, I’m here to help with more detailed guidance or code examples. Just let me know, and I can also connect you with a specialist if needed!