PIE
- 27 November 2020
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Graphics
- Vol. 39 (6), 1-14
- https://doi.org/10.1145/3414685.3417803
Abstract
Editing of portrait images is a very popular and important research topic with a large variety of applications. For ease of use, control should be provided via a semantically meaningful parameterization that is akin to computer animation controls. The vast majority of existing techniques do not provide such intuitive and fine-grained control, or only enable coarse editing of a single isolated control parameter. Very recently, high-quality semantically controlled editing has been demonstrated, however only on synthetically created StyleGAN images. We present the first approach for embedding real portrait images in the latent space of StyleGAN, which allows for intuitive editing of the head pose, facial expression, and scene illumination in the image. Semantic editing in parameter space is achieved based on StyleRig, a pretrained neural network that maps the control space of a 3D morphable face model to the latent space of the GAN. We design a novel hierarchical non-linear optimization problem to obtain the embedding. An identity preservation energy term allows spatially coherent edits while maintaining facial integrity. Our approach runs at interactive frame rates and thus allows the user to explore the space of possible edits. We evaluate our approach on a wide set of portrait photos, compare it to the current state of the art, and validate the effectiveness of its components in an ablation study.Keywords
This publication has 35 references indexed in Scilit:
- Image Style Transfer Using Convolutional Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Deep Automatic Portrait MattingPublished by Springer Science and Business Media LLC ,2016
- Perceptual Losses for Real-Time Style Transfer and Super-ResolutionPublished by Springer Science and Business Media LLC ,2016
- Painting style transfer for head portraits using convolutional neural networksACM Transactions on Graphics, 2016
- Style transfer for headshot portraitsACM Transactions on Graphics, 2014
- FaceWarehouse: A 3D Facial Expression Database for Visual ComputingIEEE Transactions on Visualization and Computer Graphics, 2013
- The Digital Emily Project: Achieving a Photorealistic Digital ActorIEEE Computer Graphics and Applications, 2010
- Post-production facial performance relighting using reflectance transferACM Transactions on Graphics, 2007
- Post-production facial performance relighting using reflectance transferACM Transactions on Graphics, 2007
- A morphable model for the synthesis of 3D facesPublished by Association for Computing Machinery (ACM) ,1999