create 3d-gltf face-mesh from image with mediapipe
Select and view 3D objects from a dataset
Turn 2D images into 3D models
Create a dynamic torus knot scene with random properties
Scalable and Versatile 3D Generation from images
create games with AI
Run a web app for creating 3D models
Create a 3D model from an image in 10 seconds!
Explore and vote on 3D arenas in a leaderboard
Display 3D recursive polygons and math functions
Explore Minnesota in a 3D VR environment
Play a 3D breakout game
Play an interactive 3D Pong game
Mediapipe Face Mesh 3D is a powerful tool that enables the creation of 3D GL Transmission Format (GLTF) face meshes from 2D images. It leverages MediaPipe's robust framework to analyze facial features and generate accurate 3D representations. This technology is particularly useful for applications in augmented reality (AR), 3D modeling, and facial animation.
pip install mediapipe
import mediapipe as mp
import cv2
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(max_num_faces=1)
image = cv2.cvtColor(cv2.imread("input_image.jpg"), cv2.COLOR_BGR2RGB)
results = face_mesh.process(image)
Can MediaPipe Face Mesh 3D work with live video?
Yes, MediaPipe Face Mesh 3D can process live video streams in real-time, making it suitable for AR and interactive applications.
What kind of input images does MediaPipe Face Mesh 3D support?
It supports standard image formats like JPEG, PNG, and BMP. The image should contain a clear view of the face for optimal results.
Can I customize the 3D mesh output?
Yes, the output mesh can be customized using external 3D modeling tools. MediaPipe provides the base mesh, which can be further refined or modified as needed.