# Facial Expression Editing with Continuous Emotion Labels

arXiv: https://arxiv.org/abs/2006.12210 · HTML: https://mlsanity.com/paper/2006.12210/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Emotional Speech-Driven Animation with Content-Emotion Disentanglement](https://mlsanity.com/paper/2306.08990/) (2023)
- [Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos](https://mlsanity.com/paper/2112.00585/) (2021)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Photorealistic and Identity-Preserving Image-Based Emotion Manipulation with Latent Diffusion Models](https://mlsanity.com/paper/2308.03183/) (2023)
- [FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion](https://mlsanity.com/paper/2505.15313/) (2025)
- [ID-Consistent, Precise Expression Generation with Blendshape-Guided Diffusion](https://mlsanity.com/paper/2510.04706/) (2025)

## Abstract (arXiv)

Recently deep generative models have achieved impressive results in the field of automated facial expression editing. However, the approaches presented so far presume a discrete representation of human emotions and are therefore limited in the modelling of non-discrete emotional expressions. To overcome this limitation, we explore how continuous emotion representations can be used to control automated expression editing. We propose a deep generative model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels. One dimension represents an emotion's valence, the other represents its degree of arousal. We demonstrate the functionality of our model with a quantitative analysis using classifier networks as well as with a qualitative analysis.