Electrical engineering

Computer animation

Gene

High fidelity

Computer graphics (images)

Engineering

Computer facial animation

Artificial intelligence

Facial expression

Generative model

Chemistry

Programming language

Transformation (genetics)

Computer science

Animation

Expression (computer science)

Computer vision

Generative grammar

Biochemistry

ACM Transactions on Graphics

This paper introduces a novel method for realtime portrait animation in a single photo. Our method requires only a single portrait photo and a set of facial landmarks derived from a driving source (e.g., a photo or a video sequence), and generates an animated image with rich facial details. The core of our method is a warp-guided generative model that instantly fuses various fine facial details (e.g., creases and wrinkles), which are necessary to generate a high-fidelity facial expression, onto a pre-warped image. Our method factorizes out the nonlinear geometric transformations exhibited in facial expressions by lightweight 2D warps and leaves the appearance detail synthesis to conditional generative neural networks for high-fidelity facial animation generation. We show such a factorization of geometric transformation and appearance synthesis largely helps the network better learn the high nonlinearity of the facial expression functions and also facilitates the design of the network architecture. Through extensive experiments on various portrait photos from the Internet, we show the significant efficacy of our method compared with prior arts.

Warp-guided GANs for single-photo facial animation