Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression (1703.07834v2)

Abstract: 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Testing code will be made available online, along with pre-trained models http://aaronsplace.co.uk/papers/jackson2017recon

• The paper introduces a novel direct CNN method that maps 2D image pixels to dense 3D facial volumes.
• It employs a stacked hourglass network architecture with landmark-guided variants to achieve improved reconstruction accuracy.
• The approach simplifies conventional pipelines and offers significant potential for AR, virtual communication, and biometric authentication.

Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression

This paper presents an innovative approach to the complex problem of 3D face reconstruction from a single 2D image, leveraging advancements in Convolutional Neural Networks (CNNs) for direct volumetric regression. Traditionally, 3D face reconstruction has required complex pipelines and multiple images due to the challenges posed by varying facial poses, expressions, and non-uniform lighting. The authors propose a method that sidesteps these difficulties by directly learning the mapping from 2D image pixels to 3D coordinates of facial geometry.

Methodology

The proposed method utilizes a simple CNN architecture to reconstruct 3D facial geometry directly from a single 2D image. This approach focuses on direct 3D volume regression, bypassing the conventional 3D Morphable Model (3DMM) fitting, which typically involves intricate optimizations. The authors introduce a novel volumetric representation for 3D faces, allowing the CNN to generate a dense 3D volume that encompasses the entire facial structure, including non-visible areas.

Significantly, the CNN architecture employed is built upon the stacked hourglass network, known for its efficacy in semantic segmentation. The network is trained end-to-end, using a dataset aligning 2D images with 3D facial scans. Notably, the paper introduces variations of the proposed architecture: the basic Volumetric Regression Network (VRN), VRN with multi-task learning for landmark localization, and VRN guided by facial landmarks, which showed improved performance due to the additional spatial guidance.

Results and Contributions

The results demonstrate substantial advancements over prior methods, with the CNN successfully reconstructing 3D faces from uncalibrated, unconstrained images with arbitrary poses and expressions. The paper reports significant improvements in reconstruction accuracy over existing techniques like 3DMM fitting, as evaluated on datasets including AFLW2000-3D, BU-4DFE, and Florence. The authors highlight the VRN - Guided model as the most effective variant, attributing its success to the inclusion of facial landmarks for enhanced spatial context.

Implications and Future Directions

The implications of this research extend to practical applications, notably in fields such as augmented reality, virtual communications, and biometric authentication, where accurate 3D face models are crucial. Theoretically, the work supports the feasibility of direct volumetric CNN regression for complex vision tasks, challenging the conventional dependency on iterative optimization-based methods.

For future research, the integration of more sophisticated datasets with finer granularity may improve the capture of intricate facial details, potentially leading to more refined and realistic facial reconstructions. Furthermore, exploring models that incorporate contextual cues from surrounding environments could enhance the robustness and accuracy of 3D reconstructions, especially under challenging conditions like extreme lighting or partial occlusions.

In conclusion, this paper provides substantial contributions to the domain of computer vision by simplifying 3D face reconstruction pipelines and demonstrating the viability of direct volume regression using CNNs, indicating promising avenues for continued exploration and application in AI and beyond.