Domain-Adaptive Neural Network for 3D Facial Reconstruction from Depth Images

Thursday - March 14 2024, 13:52 UTC - 8 months ago

A research team has developed a new method for 3D facial reconstruction from depth images, using a domain-adaptive approach that combines deep learning with auto-labeled synthetic and unlabeled real data. This method improves accuracy and robustness compared to traditional techniques and has shown promising results in challenging real-world datasets.

Facial reconstruction from visuals is a crucial task in digital face modeling and manipulation. Traditional methods often rely on RGB images, which are susceptible to lighting variations and do not provide direct 3D information. This can result in less accurate reconstructions and difficulties in manipulation. In contrast, depth images are resistant to lighting changes and directly capture 3D data, making them a more robust option for facial reconstruction. However, the scarcity of real depth images with accurate 3D labels has posed a challenge for training deep learning models in this field.

To address this issue, a research team led by Xiaoxu Cai has developed a novel approach for 3D facial reconstruction from depth images. Their findings were published in Frontiers of Computer Science on 15 Feb 2024. The proposed method uses a combination of deep learning and auto-labeled synthetic and unlabeled real data in a domain-adaptive framework. This allows for more accurate and robust 3D facial reconstruction from individual depth images captured in the real world.

The key component of their method is domain-adaptive neural networks that are specifically trained to predict head pose and facial shape. The head pose network is trained using a fine-tuning method, while the facial shape network employs an adversarial domain adaptation approach for more robust training. The initial preprocessing step involves converting pixel values from the depth image into 3D point coordinates, allowing for the use of 2D convolutions in the reconstruction network. The network output uses 3D vertex offsets, which improves the learning process by establishing a more focused target distribution.

To demonstrate the effectiveness of their method, the research team conducted thorough evaluations on challenging real-world datasets. Their results showed that their approach outperforms state-of-the-art techniques for 3D facial reconstruction from depth images, providing more accurate and robust reconstructions for digital face modeling and manipulation.