# A Review of Affective Generation Models

arXiv: https://arxiv.org/abs/2202.10763 · HTML: https://mlsanity.com/paper/2202.10763/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 187 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 30 of 32)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [ReenactGAN: Learning to Reenact Faces via Boundary Transfer](https://mlsanity.com/paper/1807.11079/) (2018)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://mlsanity.com/paper/1704.06851/) (2017)
- [ExprGAN: Facial Expression Editing with Controllable Expression Intensity](https://mlsanity.com/paper/1709.03842/) (2017)
- [Content preserving text generation with attribute controls](https://mlsanity.com/paper/1811.01135/) (2018)
- [Emotional End-to-End Neural Speech Synthesizer](https://mlsanity.com/paper/1711.05447/) (2017)
- [Geometry Guided Adversarial Facial Expression Synthesis](https://mlsanity.com/paper/1712.03474/) (2017)
- [Every Smile is Unique: Landmark-Guided Diverse Smile Generation](https://mlsanity.com/paper/1802.01873/) (2018)
- [MorpheuS: generating structured music with constrained patterns and tension](https://mlsanity.com/paper/1812.04832/) (2018)
- [An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss](https://mlsanity.com/paper/1811.07078/) (2018)
- [Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets](https://mlsanity.com/paper/1907.10087/) (2019)
- [Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey](https://mlsanity.com/paper/1911.05609/) (2019)
- [Affective Neural Response Generation](https://mlsanity.com/paper/1709.03968/) (2017)
- [Geometry-Contrastive GAN for Facial Expression Transfer](https://mlsanity.com/paper/1802.01822/) (2018)
- [Learning to Generate Customized Dynamic 3D Facial Expressions](https://mlsanity.com/paper/2007.09805/) (2020)
- [Photorealistic Facial Expression Synthesis by the Conditional Difference Adversarial Autoencoder](https://mlsanity.com/paper/1708.09126/) (2017)
- [Context based Text-generation using LSTM networks](https://mlsanity.com/paper/2005.00048/) (2020)
- [Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation](https://mlsanity.com/paper/2104.08857/) (2021)
- [Semantic Facial Expression Editing using Autoencoded Flow](https://mlsanity.com/paper/1611.09961/) (2016)
- [Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network](https://mlsanity.com/paper/1803.07716/) (2018)
- [Physiological and Affective Computing through Thermal Imaging: A Survey](https://mlsanity.com/paper/1908.10307/) (2019)
- [All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network](https://mlsanity.com/paper/1911.07050/) (2019)
- [A Survey on Mobile Affective Computing](https://mlsanity.com/paper/1410.1648/) (2014)

## Built on by (most cited first, 3 of 3)

- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Emotion-o1: Adaptive Long Reasoning for Emotion Understanding in LLMs](https://mlsanity.com/paper/2505.22548/) (2025)
- [Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers](https://mlsanity.com/paper/2408.06346/) (2024)

## Abstract (arXiv)

Affective computing is an emerging interdisciplinary field where computational systems are developed to analyze, recognize, and influence the affective states of a human. It can generally be divided into two subproblems: affective recognition and affective generation. Affective recognition has been extensively reviewed multiple times in the past decade. Affective generation, however, lacks a critical review. Therefore, we propose to provide a comprehensive review of affective generation models, as models are most commonly leveraged to affect others' emotional states. Affective computing has gained momentum in various fields and applications, thanks to the leap of machine learning, especially deep learning since 2015. With critical models introduced, this work is believed to benefit future research on affective generation. We conclude this work with a brief discussion on existing challenges.