Learning Detailed Face Reconstruction from a Single Image (1611.05053v2)

Abstract: Reconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. The reconstruction task is challenging as human faces vary extensively when considering expressions, poses, textures, and intrinsic geometries. While many approaches tackle this complexity by using additional data to reconstruct the face of a single subject, extracting facial surface from a single image remains a difficult problem. As a result, single-image based methods can usually provide only a rough estimate of the facial geometry. In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image. For this purpose, we introduce an end-to-end CNN framework which derives the shape in a coarse-to-fine fashion. The proposed architecture is composed of two main blocks, a network that recovers the coarse facial geometry (CoarseNet), followed by a CNN that refines the facial features of that geometry (FineNet). The proposed networks are connected by a novel layer which renders a depth image given a mesh in 3D. Unlike object recognition and detection problems, there are no suitable datasets for training CNNs to perform face geometry reconstruction. Therefore, our training regime begins with a supervised phase, based on synthetic images, followed by an unsupervised phase that uses only unconstrained facial images. The accuracy and robustness of the proposed model is demonstrated by both qualitative and quantitative evaluation tests.

• The paper presents a two-stage CNN architecture (CoarseNet and FineNet) that accurately reconstructs detailed 3D facial geometry from a single image.
• The method leverages a synthetic dataset for supervised learning of coarse facial parameters and unsupervised shape-from-shading for refining fine details.
• Experimental results demonstrate that the approach outperforms state-of-the-art methods in handling varied poses and expressions under minimal input conditions.

Overview of "Learning Detailed Face Reconstruction from a Single Image"

The paper presents a robust method for detailed 3D face reconstruction from a single image using a convolutional neural network (CNN) approach. The reconstruction of facial geometry from a single image has significant implications for numerous applications in computer graphics and vision, such as motion capture and facial reenactment. Unlike traditional methods that require additional data like multiple images or videos, this paper proposes an end-to-end neural network framework to accurately reconstruct detailed 3D facial geometries using only a single image.

Methodology

The paper introduces a two-stage CNN architecture that processes input images in a coarse-to-fine manner. The architecture is divided into two primary components: CoarseNet and FineNet.

1. CoarseNet: This network is tasked with estimating the coarse geometry and pose of the face. Utilizing a dataset of synthetic images, CoarseNet is trained to predict the initial parameters representing facial identity and expression based on a 3D Morphable Model (3DMM). The authors implement an iterative approach where feedback channels (e.g., Projected Normalized Coordinate Code and normal maps) are used to refine predictions in successive iterations.
2. FineNet: Operating on detailed depth maps, this network is responsible for extracting and refining fine-grained facial features like wrinkles and textures. To achieve this, a novel rendering layer translates the 3DMM output from CoarseNet into depth maps, allowing FineNet to refine these details through shape-from-shading techniques under an unsupervised training regime.

Training and Data

A key challenge addressed by the paper is the lack of suitable real datasets with detailed facial geometries. The research compensates for this by creating a synthetic dataset to enable the supervised training of CoarseNet. FineNet, however, leverages an unsupervised learning approach based on axiomatic models like shape-from-shading, eliminating dependency on labeled training data for fine detail reconstruction.

Results and Implications

Experimental results show that the proposed method effectively reconstructs detailed facial structures from single images, outperforming existing state-of-the-art techniques in both qualitative and quantitative metrics. The paper highlights that the method is robust against variations in facial expressions and poses, demonstrating significant potential for real-world applications where only minimal data input is available.

Future Directions

This research sets a foundation for further advancements in single-image 3D reconstruction. Future work could explore extending the capability of such neural networks to handle more diverse facial features and environments by enhancing synthetic data generation techniques or integrating additional semantic context into the network’s learning paradigm. As CNN architectures continue evolving, the framework presented could leverage new developments to enhance accuracy and reliability further.

In summary, this paper makes a significant contribution to the field of facial geometry reconstruction by introducing a sophisticated CNN approach capable of deriving detailed 3D representations from single facial images, with implications for advancing applications in virtual reality, animation, and beyond.