# The Florence 4D Facial Expression Dataset

arXiv: https://arxiv.org/abs/2210.16807 · HTML: https://mlsanity.com/paper/2210.16807/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 30 Oct 2022.

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

- [Generating 3D faces using Convolutional Mesh Autoencoders](https://mlsanity.com/paper/1807.10267/) (2018)
- [Capture, Learning, and Synthesis of 3D Speaking Styles](https://mlsanity.com/paper/1905.03079/) (2019)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation](https://mlsanity.com/paper/1905.02876/) (2019)
- [Sparse to Dense Dynamic 3D Facial Expression Generation](https://mlsanity.com/paper/2105.07463/) (2021)
- [A Sparse and Locally Coherent Morphable Face Model for Dense Semantic Correspondence Across Heterogeneous 3D Faces](https://mlsanity.com/paper/2006.03840/) (2020)
- [Learning to Generate Customized Dynamic 3D Facial Expressions](https://mlsanity.com/paper/2007.09805/) (2020)

## Built on by (most cited first, 8 of 8)

- [EmoVOCA: Speech-Driven Emotional 3D Talking Heads](https://mlsanity.com/paper/2403.12886/) (2024)
- [Neuromorphic Facial Analysis with Cross-Modal Supervision](https://mlsanity.com/paper/2409.10213/) (2024)
- [Beyond Fixed Topologies: Unregistered Training and Comprehensive Evaluation Metrics for 3D Talking Heads](https://mlsanity.com/paper/2410.11041/) (2024)
- [Express4D: Expressive, Friendly, and Extensible 4D Facial Motion Generation Benchmark](https://mlsanity.com/paper/2508.12438/) (2025)
- [Spatio-temporal Transformers for Action Unit Classification with Event Cameras](https://mlsanity.com/paper/2410.21958/) (2024)
- [Learning Disentangled Speech- and Expression-Driven Blendshapes for 3D Talking Face Animation](https://mlsanity.com/paper/2510.25234/) (2025)
- [KeyframeFace: Language-Driven Facial Animation via Semantic Keyframes](https://mlsanity.com/paper/2512.11321/) (2025)
- [Capturing Dynamics: The 4D Facial Expression Intensity Dataset](https://mlsanity.com/paper/2610.02647/) (2026)

## Abstract (arXiv)

Human facial expressions change dynamically, so their recognition / analysis should be conducted by accounting for the temporal evolution of face deformations either in 2D or 3D. While abundant 2D video data do exist, this is not the case in 3D, where few 3D dynamic (4D) datasets were released for public use. The negative consequence of this scarcity of data is amplified by current deep learning based-methods for facial expression analysis that require large quantities of variegate samples to be effectively trained. With the aim of smoothing such limitations, in this paper we propose a large dataset, named Florence 4D, composed of dynamic sequences of 3D face models, where a combination of synthetic and real identities exhibit an unprecedented variety of 4D facial expressions, with variations that include the classical neutral-apex transition, but generalize to expression-to-expression. All these characteristics are not exposed by any of the existing 4D datasets and they cannot even be obtained by combining more than one dataset. We strongly believe that making such a data corpora publicly available to the community will allow designing and experimenting new applications that were not possible to investigate till now. To show at some extent the difficulty of our data in terms of different identities and varying expressions, we also report a baseline experimentation on the proposed dataset that can be used as baseline.