Semantic Facial Expression Editing using Autoencoded Flow

Abstract

High-level manipulation of facial expressions in images --- such as changing a smile to a neutral expression --- is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face. We present a fully automatic approach to editing faces that combines the advantages of flow-based face manipulation with the more recent generative capabilities of Variational Autoencoders (VAEs). During training, our model learns to encode the flow from one expression to another over a low-dimensional latent space. At test time, expression editing can be done simply using latent vector arithmetic. We evaluate our methods on two applications: 1) single-image facial expression editing, and 2) facial expression interpolation between two images. We demonstrate that our method generates images of higher perceptual quality than previous VAE and flow-based methods.

References in corpus (3)

• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Fully Connected Deep Structured Networks
• DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation

Cited by in corpus (10)

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• Region Based Adversarial Synthesis of Facial Action Units
• All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network
• Unconstrained Facial Expression Transfer using Style-based Generator
• Facial Expression Editing with Continuous Emotion Labels
• Automated Deepfake Detection
• Learning Symmetry Consistent Deep CNNs for Face Completion
• Lie Group Auto-Encoder
• Joint Deep Learning of Facial Expression Synthesis and Recognition
• Learning Sparsity of Representations with Discrete Latent Variables