Hair Styles - segmentation - paper reads

Paper #1 - Barbershop - Segmentation Masks

Git code  - Link

Notes

• GAN-based semantic alignment step which generates high quality images similar to the
• input images
• The shape of the hair is the binary segmentation region, and the
• identity of a head-image

Architecture Ref

• segmentation network such as BiSeNET
• alignment in 𝑊 + space
• a close-up view of the face (top) and hair (bottom) in 𝑊 + space
• close-up views after details are transferred

(1) Reconstruction: A latent code Crec found to reconstruct the input image I𝑘

(2) Alignment: A nearby latent code C align is found that minimizes the cross-entropy between the generated image and the target mask M.

• manipulating segmentation masks and copying content from different reference images.
• Copy and replace eyes / nose / lips area pixels and values
• Copy the landmark areas / eyes / face lips
• Copy all boundaries of key facial landmarks

Face parsing - link

Paper #2 - Face shape classification using Inception v3

Paper #3 - CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation based on CelebA

Paper - #4 - Fashion Meets Computer Vision: A Survey

Products

Beauty Opportunities

• LOHO: Latent Optimization of Hairstyles via Orthogonalization
• MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
• K-HAIRSTYLE: A LARGE-SCALE KOREAN HAIRSTYLE DATASET FOR VIRTUAL HAIR EDITING AND HAIRSTYLE CLASSIFICATION
• VOGUE: Try-On by StyleGAN Interpolation Optimization

Project - Link

To deploy this app, follow the procedure below in a GCP console terminal:

• Clone repository: git clone https://github.com/shawnhan108/BiSeNet-app.git.
• Set project ID: export PROJECT_ID=bisenet.
• Build docker image: docker build -t gcr.io/bisenet/bisenet-app:v1 ..
• Authorize docker: gcloud auth configure-docker.
• Push docker image to Container Registry: docker push gcr.io/bisenet/bisenet-app:v1.
• Set computing region: gcloud config set compute/zone us-central1-a.
• Create a Kubernetes Engine cluster: gcloud container clusters create bisenet-cluster --num-nodes=2.
• Create a Kubernetes deployment of the app: kubectl create deployment bisenet-app --image=gcr.io/bisenet/bisenet-app:v1.
• Expose the app with a Load Balancer service: kubectl expose deployment bisenet-app --type=LoadBalancer --port 80 --target-port 8080.
• Go to the browser and test the app: http://[EXTERNAL-IP], where EXTERNAL-IP can be obtained using kubectl get service.

Keep Thinking!!!