# Semantic Facial Expression Editing using Autoencoded Flow

arXiv: https://arxiv.org/abs/1611.09961 · HTML: https://mlsanity.com/paper/1611.09961/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

## Built on by (most cited first, 7 of 7)

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- [GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images](https://mlsanity.com/paper/2308.03413/) (2023)
- [Learning Sparsity of Representations with Discrete Latent Variables](https://mlsanity.com/paper/2304.00935/) (2023)
- [The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles](https://mlsanity.com/paper/2409.15750/) (2024)

## Abstract (arXiv)

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.