3D Face Modeling From Diverse Raw Scan Data (1902.04943v3)

Abstract: Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database. The main roadblock of building a large-scale face model from diverse 3D databases lies in the lack of dense correspondence among raw scans. To address these problems, this paper proposes an innovative framework to jointly learn a nonlinear face model from a diverse set of raw 3D scan databases and establish dense point-to-point correspondence among their scans. Specifically, by treating input scans as unorganized point clouds, we explore the use of PointNet architectures for converting point clouds to identity and expression feature representations, from which the decoder networks recover their 3D face shapes. Further, we propose a weakly supervised learning approach that does not require correspondence label for the scans. We demonstrate the superior dense correspondence and representation power of our proposed method, and its contribution to single-image 3D face reconstruction.

• The paper introduces a novel weakly supervised framework using PointNet to robustly model 3D faces from diverse raw scans, addressing the challenge of correspondence in varied datasets.
• It employs an encoder-decoder using PointNet to interpret raw scan point clouds, reconstructing shapes and providing dense correspondence among scans with varying expressions and resolutions.
• Experimental results show improved dense correspondence accuracy, faster processing, and better performance than traditional methods, enabling enhanced applications like single-image reconstruction and facial recognition.

Overview of 3D Face Modeling From Diverse Raw Scan Data

This paper introduces a novel framework for the robust modeling of 3D faces from diverse raw scan data, addressing longstanding challenges in the field of computer vision. Traditional methods in 3D face modeling have relied heavily on linear subspace learning from a limited set of scans within a single database. A critical impediment to creating more comprehensive large-scale models has been the difficulty in establishing dense point-to-point correspondence across diverse datasets.

To overcome these limitations, the authors propose an innovative approach that uses PointNet architectures to interpret raw scans as unorganized point clouds, translating them into identity and expression feature representations. These representations are then decoded to reconstruct 3D face shapes. The proposed framework is distinctive in its ability to integrate multiple 3D face databases without requiring correspondence labels for the scans, thus employing a weakly supervised learning strategy.

Methodology

The methodology encompasses several key components:

• Encoder-Decoder Framework: The encoder leverages PointNet, a neural network architecture designed for deep learning on point clouds, to encode raw scan data into compact identity and expression representations. This is followed by decoder networks that reconstruct the 3D face shape from these representations. The framework excels in providing dense correspondence among scans with varying expressions and resolutions.
• Weakly Supervised Learning: The paper advances a weakly supervised learning approach using synthetic data, where ground-truth correspondences are known, to create shape correspondence priors. It also incorporates unsupervised training with real scans using order-invariant loss functions, such as the Chamfer distance, to guide the learning of dense correspondences.
• Loss Functions: The comprehensive loss framework ensures high fidelity in reconstructed 3D shapes, incorporating vertex loss, surface normal loss, edge length regularization, and region-specific constraints.

Experimental Results

Quantitative evaluations demonstrate the enhanced performance of the proposed approach over traditional methods. On the BU3DFE database, the semantic landmark error was significantly reduced, illustrating improved dense correspondence accuracy compared to state-of-the-art methods. Furthermore, the application of this framework to high-resolution databases like FRGC v2.0 showcased the ability to maintain high-frequency details in 3D models, while achieving faster processing than existing techniques.

From a representation perspective, the method offers superior compactness and accuracy in expressing both identity and extreme expression variations, when benchmarked against existing linear and nonlinear models across several datasets.

Implications and Future Directions

The implications of this research stretch across several practical and theoretical domains. Practically, this modeling technique can substantially enhance applications such as single-image 3D face reconstruction, facial recognition algorithms, and even character animation in graphics. The theoretical implications suggest advancements in how raw data can inform model precision in non-linear settings.

Future developments could explore the integration of more elaborate neural architectures and loss functions that further refine the detail and nuance of reconstructed face models, potentially expanding applications to other object classes beyond facial data. Additionally, more extensive datasets could bolster model generalization, improving performance across ethnic and demographic variances. This work paves the way for increasingly automated and robust solutions in 3D morphable modeling, promising expanding capabilities in AI-driven visual comprehension tasks.