Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation

Abstract: In this work, we aim to enhance model-based face reconstruction by avoiding fitting the model to outliers, i.e. regions that cannot be well-expressed by the model such as occluders or make-up. The core challenge for localizing outliers is that they are highly variable and difficult to annotate. To overcome this challenging problem, we introduce a joint Face-autoencoder and outlier segmentation approach (FOCUS).In particular, we exploit the fact that the outliers cannot be fitted well by the face model and hence can be localized well given a high-quality model fitting. The main challenge is that the model fitting and the outlier segmentation are mutually dependent on each other, and need to be inferred jointly. We resolve this chicken-and-egg problem with an EM-type training strategy, where a face autoencoder is trained jointly with an outlier segmentation network. This leads to a synergistic effect, in which the segmentation network prevents the face encoder from fitting to the outliers, enhancing the reconstruction quality. The improved 3D face reconstruction, in turn, enables the segmentation network to better predict the outliers. To resolve the ambiguity between outliers and regions that are difficult to fit, such as eyebrows, we build a statistical prior from synthetic data that measures the systematic bias in model fitting. Experiments on the NoW testset demonstrate that FOCUS achieves SOTA 3D face reconstruction performance among all baselines that are trained without 3D annotation. Moreover, our results on CelebA-HQ and the AR database show that the segmentation network can localize occluders accurately despite being trained without any segmentation annotation.

• The paper presents the FOCUS framework, which integrates face autoencoding and unsupervised outlier segmentation to address occlusions in 3D face reconstruction.
• It employs an EM-style iterative training process that refines both reconstruction and segmentation, achieving state-of-the-art results on multiple benchmarks.
• The approach minimizes reliance on extensive labeled data, offering a scalable solution for handling occlusions and adverse imaging conditions.

Insights into Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation

This paper presents a notable advancement in the domain of model-based 3D face reconstruction, introducing a novel methodology that effectively addresses outlier interference by utilizing a weakly-supervised outlier segmentation technique. The central contribution of this work is the integrated framework named FOCUS, which synergistically combines the tasks of face reconstruction and outlier segmentation in a mutually beneficial strategy, ultimately enhancing overall performance without the need for extensive labeled data.

Summary of Methodology

The challenge addressed by the authors lies in the difficulty of accurately reconstructing 3D faces from images, especially when the faces are perturbed by external factors such as occlusions, makeup, or unusual facial features. Traditional model-based autoencoders often struggle with this issue due to their tendency to fit these model outliers, leading to distortions in the reconstruction output.

The paper introduces a joint Face-autoencoder and Outlier Segmentation Network called FOCUS. This integrated approach captures the intricate dependency between facial reconstruction and outlier detection. Fundamentally, the framework follows an Expectation-Maximization (EM)-style training process, wherein model fitting and outlier segmentation are iteratively refined to enhance each other's outputs. The segmentation network, trained using an unsupervised method, predicts a mask identifying regions of the target image that fit poorly to the morphological model, denoting these as outliers.

Numerical Results

The empirical performance of the FOCUS framework is thoroughly validated through experiments conducted on multiple benchmark datasets. On the NoW test set, the proposed method achieves state-of-the-art (SOTA) accuracy in 3D face reconstruction, competing favorably against all baselines trained without explicit 3D supervision. Additionally, in tests on datasets such as CelebA-HQ and the AR database, the segmentation network within FOCUS excels at accurately detecting and localizing occlusions despite the lack of explicit segmentation annotations during training.

Analysis of Contributions

The authors introduce a statistical prior based on synthetic data to mitigate ambiguity between outliers and challenging-to-fit regions, effectively accounting for systematic biases in model fitting. The paper’s contributions are thus threefold:

1. Robust Reconstruction: It sets a new benchmark in model-based 3D face reconstruction by introducing a robust framework that requires minimal assumptions about the data, thereby improving the ability to operate in diverse real-world scenarios.
2. Innovative Segmentation Strategy: The introduction of an outlier segmentation network that requires no explicit labelling is a significant step forward. It highlights the potential to leverage model limitations as a learning signal, pushing the boundaries of unsupervised learning paradigms.
3. Expectation-Maximization Approach: The EM-type joint training and inference of face autoencoders and segmentation networks demonstrate effective mutual reinforcement, which can serve as an inspiring paradigm for similar joint learning tasks in other fields of computer vision and AI.

Implications and Future Directions

The work significantly advances the robustness of model-based face reconstruction frameworks. Its implications extend beyond face reconstruction and segmentation tasks. The FOCUS approach can potentiate the development of similar methodologies in other domains, for instance, general object reconstruction under occlusions or unforeseen visual perturbations. Furthermore, the unsupervised segmentation strategy has the potential to alleviate the reliance on labeled data, propelling advancements in scenarios with limited annotations.

While the framework excels in current benchmarks, its dependence on the underlying face model's expressiveness may limit its applicability in scenarios containing intricate facial features or extreme occlusions. Future work could enhance these limitations by integrating more expressive non-linear face models or refining model's latent spaces tailored for specific domains.

Overall, the introduction of the FOCUS framework marks a promising step toward more resilient and autonomous 3D vision systems, stimulating future research into unsupervised learning models and their application in complex visual tasks.