# FaceDet3D: Facial Expressions with 3D Geometric Detail Prediction

arXiv: https://arxiv.org/abs/2012.07999 · HTML: https://mlsanity.com/paper/2012.07999/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [RigNeRF: Fully Controllable Neural 3D Portraits](https://mlsanity.com/paper/2206.06481/) (2022)
- [FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation](https://mlsanity.com/paper/2108.04913/) (2021)
- [JEAN: Joint Expression and Audio-guided NeRF-based Talking Face Generation](https://mlsanity.com/paper/2409.12156/) (2024)
- [Controllable Dynamic Appearance for Neural 3D Portraits](https://mlsanity.com/paper/2309.11009/) (2023)

## Abstract (arXiv)

Facial Expressions induce a variety of high-level details on the 3D face geometry. For example, a smile causes the wrinkling of cheeks or the formation of dimples, while being angry often causes wrinkling of the forehead. Morphable Models (3DMMs) of the human face fail to capture such fine details in their PCA-based representations and consequently cannot generate such details when used to edit expressions. In this work, we introduce FaceDet3D, a first-of-its-kind method that generates - from a single image - geometric facial details that are consistent with any desired target expression. The facial details are represented as a vertex displacement map and used then by a Neural Renderer to photo-realistically render novel images of any single image in any desired expression and view. The project website is: http://shahrukhathar.github.io/2020/12/14/FaceDet3D.html