# EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation

arXiv: https://arxiv.org/abs/2401.06957 · HTML: https://mlsanity.com/paper/2401.06957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal distillation, one of 12 directions in Knowledge distillation.
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Placed in

- Knowledge distillation / [Multimodal distillation](https://mlsanity.com/distill/multimodal-distillation/)

## Builds on (most cited first, 7 of 7)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances](https://mlsanity.com/paper/2203.06935/) (2022)
- [sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification](https://mlsanity.com/paper/2108.10566/) (2021)
- [4D Attention-based Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2101.05484/) (2021)
- [Emotion Recognition with Machine Learning Using EEG Signals](https://mlsanity.com/paper/1903.07272/) (2019)

## Built on by (most cited first, 1 of 1)

- [Mind-to-Face: Neural-Driven Photorealistic Avatar Synthesis via EEG Decoding](https://mlsanity.com/paper/2512.04313/) (2025)

## Abstract (arXiv)

As virtual environments continue to advance, the demand for immersive and emotionally engaging experiences has grown. Addressing this demand, we introduce Emotion enabled Virtual avatar mapping using Optimized KnowledgE distillation (EVOKE), a lightweight emotion recognition framework designed for the seamless integration of emotion recognition into 3D avatars within virtual environments. Our approach leverages knowledge distillation involving multi-label classification on the publicly available DEAP dataset, which covers valence, arousal, and dominance as primary emotional classes. Remarkably, our distilled model, a CNN with only two convolutional layers and 18 times fewer parameters than the teacher model, achieves competitive results, boasting an accuracy of 87% while demanding far less computational resources. This equilibrium between performance and deployability positions our framework as an ideal choice for virtual environment systems. Furthermore, the multi-label classification outcomes are utilized to map emotions onto custom-designed 3D avatars.