Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network (1803.07835v1)

Abstract: We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment. To achieve this, we design a 2D representation called UV position map which records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from a single 2D image. We also integrate a weight mask into the loss function during training to improve the performance of the network. Our method does not rely on any prior face model, and can reconstruct full facial geometry along with semantic meaning. Meanwhile, our network is very light-weighted and spends only 9.8ms to process an image, which is extremely faster than previous works. Experiments on multiple challenging datasets show that our method surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin.

• The paper introduces a novel UV position map regression technique to jointly perform 3D face reconstruction and dense alignment.
• It employs an encoder-decoder CNN with a unique weighted loss to map 3D facial coordinates from 2D images effectively.
• Experimental results show over 25% improvement in dense alignment accuracy and a processing speed of 9.8ms per image, underscoring real-time applicability.

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

The paper "Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network" introduces a novel approach for simultaneously performing 3D face reconstruction and dense alignment. The proposed method leverages a 2D representation termed as the UV position map to encapsulate the 3D facial structure, which is then regressed using a convolutional neural network (CNN) from a single 2D image.

Methodology

The UV position map is a distinct contribution of this work, recording 3D coordinates of facial points within UV space. By employing a CNN to regress this map, the method circumvents the need to rely on any pre-existing 3D face model or low-dimensional parameter spaces, commonly seen in previous approaches. The network architecture is built around an encoder-decoder framework that efficiently learns the mapping from RGB images to the 3D UV position map. A unique weighted loss function further refines this learning process, prioritizing discriminative facial regions during training.

Experimental Results

The performance of the method was validated on several challenging datasets, such as AFLW2000-3D and Florence, showing significant improvements over state-of-the-art techniques on both reconstruction and alignment tasks. Key numerical results highlight that the proposed method achieves over 25% relative improvement on dense alignment tasks. Additionally, the framework's efficiency is emphasized by its capability to process images in 9.8ms, surpassing the runtime performance of existing methods.

Implications and Future Directions

The synergy between 3D face reconstruction and dense alignment presented in this paper opens pathways for practical applications in areas like facial authentication, augmented reality, and animation. The integration of the UV position map as a representation strategy underlines the potential of leveraging spatially coherent structures in vision tasks, prompting further exploration into more elaborate geometric representations.

This work invites future research to build on its findings, potentially through incorporating more sophisticated architectures or optimizing for specific applications in real-time environments. Additionally, examining the UV position map’s applicability across other domains in computer vision could provide valuable insights into more generalized 3D geometric understanding.

Conclusion

In summary, this research addresses critical limitations of prior model-based methods by introducing an innovative representation and network approach. It pushes the boundaries of real-time capabilities in 3D face processing, demonstrating both theoretical and practical advancements in the field of computer vision.