A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images (2302.14434v2)

Abstract: Limited by the nature of the low-dimensional representational capacity of 3DMM, most of the 3DMM-based face reconstruction (FR) methods fail to recover high-frequency facial details, such as wrinkles, dimples, etc. Some attempt to solve the problem by introducing detail maps or non-linear operations, however, the results are still not vivid. To this end, we in this paper present a novel hierarchical representation network (HRN) to achieve accurate and detailed face reconstruction from a single image. Specifically, we implement the geometry disentanglement and introduce the hierarchical representation to fulfill detailed face modeling. Meanwhile, 3D priors of facial details are incorporated to enhance the accuracy and authenticity of the reconstruction results. We also propose a de-retouching module to achieve better decoupling of the geometry and appearance. It is noteworthy that our framework can be extended to a multi-view fashion by considering detail consistency of different views. Extensive experiments on two single-view and two multi-view FR benchmarks demonstrate that our method outperforms the existing methods in both reconstruction accuracy and visual effects. Finally, we introduce a high-quality 3D face dataset FaceHD-100 to boost the research of high-fidelity face reconstruction. The project homepage is at https://younglbw.github.io/HRN-homepage/.

• The paper introduces a hierarchical representation network that segments facial geometry into low, mid, and high-frequency components to capture fine details.
• It leverages face-wise, vertex-wise, and pixel-wise maps alongside 3D priors to enhance reconstruction accuracy beyond traditional 3DMM methods.
• Experimental results show superior performance with lower Chamfer Distance and Mean Normal Error, benefiting VR, AR, and digital media applications.

Hierarchical Representation Network for 3D Face Reconstruction

The paper "A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images" presents a sophisticated approach to 3D face reconstruction using a hierarchical representation network (HRN). The authors address the limitations of traditional 3D morphable models (3DMM), particularly in failing to capture high-frequency details such as wrinkles and dimples.

Methodology

The proposed HRN framework introduces a hierarchical modeling strategy, segmenting facial geometry into low-frequency, mid-frequency, and high-frequency components. This is accomplished through:

1. Face-Wise Blendshape Coefficients: For low-frequency geometry.
2. Vertex-Wise Deformation Map: For mid-frequency details, capturing larger-scale features like jaw contours.
3. Pixel-Wise Displacement Map: For high-frequency details, enabling vivid rendering of fine textures.

The network leverages image translation models to predict these components, enhancing detail reconstruction through a coarse-to-fine process.

Additionally, the incorporation of 3D priors enriches the model, aiming to ensure authenticity and accuracy. These priors are derived from face scans and utilized in both adversarial and semi-supervised learning contexts. The authors also propose a de-retouching module to disentangle geometry from face texture, refining the reconstructed appearance by distinguishing between blemishes and illuminations.

Results and Evaluation

The authors validate their approach through extensive experiments on benchmark datasets for both single-view and multi-view scenarios. The HRN consistently outperforms existing methods in terms of reconstruction accuracy and visual fidelity.

Numerical Results:

• Achieves lower Chamfer Distance (CD) and Mean Normal Error (MNE) compared to state-of-the-art models.
• Excels in Normalized Mean Square Error (NMSE) on region-specific evaluations.

Implications

The HRN's ability to perform in both single-view and multi-view contexts underscores its flexibility and robustness. This has significant implications for applications in virtual reality (VR), augmented reality (AR), and digital media creation. The introduction of a hierarchical strategy for geometry modeling also opens new pathways for improved detail reconstruction in computer vision.

Future Directions

The proposed framework encourages further exploration into:

• Enhanced multi-view amalgamation techniques for even sparse datasets.
• Extending hierarchical models to other domains requiring precision detail, such as medical imaging.
• Exploring the integration of HRN with real-time applications and interactive platforms.

Conclusion

This work represents a meaningful contribution to the domain of 3D face reconstruction by addressing both theoretical and practical challenges. The introduction of hierarchical modeling potentially sets a new standard in the quest for detailed and accurate digital representations, marking a significant step forward in the practical application of advanced computer vision technologies.