# Fitting 3D Morphable Models using Local Features

arXiv: https://arxiv.org/abs/1503.02330 · HTML: https://mlsanity.com/paper/1503.02330/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2015.

## Built on by (most cited first, 6 of 6)

- [SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction](https://mlsanity.com/paper/2106.03021/) (2021)
- [A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization](https://mlsanity.com/paper/2310.03205/) (2023)
- [Realistic face animation generation from videos](https://mlsanity.com/paper/2103.14984/) (2021)
- [CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment and Reconstruction](https://mlsanity.com/paper/2405.19659/) (2024)
- [Sketch-1-to-3: One Single Sketch to 3D Detailed Face Reconstruction](https://mlsanity.com/paper/2502.17852/) (2025)
- [Revisiting an Old Perspective Projection for Monocular 3D Morphable Models Regression](https://mlsanity.com/paper/2603.04958/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel fitting method that uses local image features to fit a 3D Morphable Model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on Morphable Model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a Morphable Model. Because of the speed of our method, it is applicable for realtime applications. Our cascaded regression framework is available as an open source library (https://github.com/patrikhuber).