On Day 4, we’ll render a 3D avatar using Three.js and map real-time face tracking data to control the avatar’s movements. This will allow the avatar to mimic expressions like blinking, smiling, and head tilting based on facial landmarks.
1. Introduction to 3D Avatar Rendering
3D avatars are animated models that mimic a user’s facial movements in real-time. We’ll use Three.js, a JavaScript library for 3D graphics, to render and animate our avatar inside a React Native app.
2. Installing Three.js in React Native
Step 1: Install React Three Fiber
React Three Fiber is a Three.js wrapper optimized for React:
npm install @react-three/fiber three
Step 2: Install Expo-GL for WebGL Support
WebGL is required to render 3D graphics in a React Native environment:
expo install expo-gl react-three-fiber
3. Creating a 3D Avatar Component
Step 1: Create AvatarRenderer.js
Inside src/components/, create a new file:
import React, { useRef } from 'react';
import { Canvas, useFrame } from '@react-three/fiber';
import { View } from 'react-native';
function AvatarModel({ facialExpressions }) {
const headRef = useRef();
useFrame(() => {
if (headRef.current) {
headRef.current.rotation.x = facialExpressions.headTilt * 0.1;
headRef.current.rotation.y = facialExpressions.headTurn * 0.1;
}
});
return (
<mesh ref={headRef}>
<sphereGeometry args={[1, 32, 32]} />
<meshStandardMaterial color="orange" />
</mesh>
);
}
export default function AvatarRenderer({ facialExpressions }) {
return (
<View style={{ flex: 1 }}>
<Canvas>
<ambientLight intensity={0.5} />
<directionalLight position={[0, 5, 5]} intensity={1} />
<AvatarModel facialExpressions={facialExpressions} />
</Canvas>
</View>
);
}
4. Integrating Avatar Rendering with Face Tracking
Step 1: Update CameraScreen.js
Modify CameraScreen.js to send facial landmarks data to AvatarRenderer.js:
import React, { useState, useEffect } from 'react';
import { View, StyleSheet } from 'react-native';
import { Camera } from 'expo-camera';
import * as tf from '@tensorflow/tfjs';
import * as faceLandmarksDetection from '@tensorflow-models/face-landmarks-detection';
import AvatarRenderer from './AvatarRenderer';
export default function CameraScreen() {
const [hasPermission, setHasPermission] = useState(null);
const [model, setModel] = useState(null);
const [facialExpressions, setFacialExpressions] = useState({
headTilt: 0,
headTurn: 0,
});
useEffect(() => {
(async () => {
const { status } = await Camera.requestPermissionsAsync();
setHasPermission(status === 'granted');
})();
}, []);
useEffect(() => {
const loadModel = async () => {
await tf.ready();
const loadedModel = await faceLandmarksDetection.load(
faceLandmarksDetection.SupportedPackages.mediapipeFacemesh
);
setModel(loadedModel);
};
loadModel();
}, []);
const detectFace = async (image) => {
if (!model) return;
const predictions = await model.estimateFaces({
input: image,
returnTensors: false,
});
if (predictions.length > 0) {
const landmarks = predictions[0].scaledMesh;
const headTilt = landmarks[33][1] - landmarks[263][1]; // Difference in eye level
const headTurn = landmarks[1][0] - (landmarks[33][0] + landmarks[263][0]) / 2; // Nose offset
setFacialExpressions({ headTilt, headTurn });
}
};
if (hasPermission === null) return <View />;
if (hasPermission === false) return <Text>No access to camera</Text>;
return (
<View style={styles.container}>
<Camera style={styles.camera} type={Camera.Constants.Type.front} />
<AvatarRenderer facialExpressions={facialExpressions} />
</View>
);
}
const styles = StyleSheet.create({
container: { flex: 1 },
camera: { flex: 1, position: 'absolute' },
});
5. Testing Avatar Movements with Face Tracking
Step 1: Run the App
expo start
Step 2: Test Real-Time Avatar Control
- Move your head left/right → Avatar should rotate accordingly.
- Tilt your head up/down → Avatar should follow.
6. Enhancing the Avatar with Facial Expressions
Step 1: Detect Blinking & Smiling
Modify detectFace to detect blinking and smiling:
const detectFacialExpressions = (landmarks) => {
const eyeDistance = Math.abs(landmarks[159][1] - landmarks[145][1]); // Eye closure distance
const mouthWidth = Math.abs(landmarks[61][0] - landmarks[291][0]); // Mouth width
return {
isBlinking: eyeDistance < 0.02,
isSmiling: mouthWidth > 0.08,
};
};
Step 2: Apply Expressions to Avatar
Update AvatarModel.js:
function AvatarModel({ facialExpressions }) {
const headRef = useRef();
const eyeRef = useRef();
useFrame(() => {
if (headRef.current) {
headRef.current.rotation.x = facialExpressions.headTilt * 0.1;
headRef.current.rotation.y = facialExpressions.headTurn * 0.1;
}
if (eyeRef.current) {
eyeRef.current.scale.y = facialExpressions.isBlinking ? 0.1 : 1; // Blink effect
}
});
return (
<group>
<mesh ref={headRef}>
<sphereGeometry args={[1, 32, 32]} />
<meshStandardMaterial color="orange" />
</mesh>
<mesh ref={eyeRef} position={[0.3, 0.5, 1]}>
<sphereGeometry args={[0.1, 16, 16]} />
<meshStandardMaterial color="black" />
</mesh>
</group>
);
}
7. Optimizing Avatar Rendering
- Reduce update frequency by processing every 2 frames:
if (frameCount % 2 === 0) detectFace(frame);
- Optimize 3D rendering by using lower polygon avatars.
8. Key Concepts Covered
✅ Rendered a 3D avatar using Three.js.
✅ Mapped head movement to avatar rotation.
✅ Detected blinking & smiling and applied them to the avatar.
9. Next Steps: Adding Real-Time AR Effects
Tomorrow, we’ll: 🔹 Add hats, glasses, and masks as AR filters.
🔹 Improve avatar customization with different skins.
10. References & Learning Resources
11. SEO Keywords:
React Native AI avatars, real-time 3D avatars, Three.js face tracking, building a VTuber app, animating avatars with face detection.
