# Brain2Face: A Study on the Relation of EEG Signals and Spatio-temporal Head Avatars

arXiv: https://arxiv.org/abs/2610.17356 · HTML: https://mlsanity.com/paper/2610.17356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 22 of 22)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians](https://mlsanity.com/paper/2312.02069/) (2023)
- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [I M Avatar: Implicit Morphable Head Avatars from Videos](https://mlsanity.com/paper/2112.07471/) (2021)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [Decoding Natural Images from EEG for Object Recognition](https://mlsanity.com/paper/2308.13234/) (2023)
- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [Machine-Learning-Based Diagnostics of EEG Pathology](https://mlsanity.com/paper/2002.05115/) (2020)
- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis](https://mlsanity.com/paper/2510.22257/) (2025)
- [S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention](https://mlsanity.com/paper/2403.11772/) (2024)
- [Audio-Driven Universal Gaussian Head Avatars](https://mlsanity.com/paper/2509.18924/) (2025)
- [Mind-to-Face: Neural-Driven Photorealistic Avatar Synthesis via EEG Decoding](https://mlsanity.com/paper/2512.04313/) (2025)
- [Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition](https://mlsanity.com/paper/2502.00547/) (2025)
- [REFA: Real-time Egocentric Facial Animations for Virtual Reality](https://mlsanity.com/paper/2601.03507/) (2026)
- [See the Emotion: A Facial Emoji Proxy Modeling for EEG Emotion Recognition](https://mlsanity.com/paper/2607.02912/) (2026)

## Abstract (arXiv)

Driving digital head avatars is classically done by hand-crafted artist design or by image-based facial performance capture. In this work, we ask whether spatio-temporal tracking of the humans's head geometry and appearance can be performed solely from brain signals, i.e., Electroencephalography (EEG). Since prior work mostly focused on activity and emotion recognition, this task is relatively unexplored without even having publicly available high-quality datasets and benchmarks. Thus, we contribute a dataset featuring paired EEG and human head geometry as well as appearance annotations. We synchronize 64-channel EEG with per-frame expression latent codes of a personalized Gaussian head avatar. Second, we introduce a baseline to the task of expression recovery of a head avatar from EEG signals. To this end, we propose a dedicated EEG-to-expression model that takes a temporal sequence of EEG signals as input and regresses the latent expression parameters of the 3D Gaussian head avatar. Lastly, we validate our capture and algorithmic design choices through a benchmark protocol that we introduce to this task. We believe this work paves the way towards better understanding of the relation between EEG signals and facial surface as well as appearance changes.