Adaptive 3D Face Reconstruction from a Single Image (2007.03979v2)

Abstract: 3D face reconstruction from a single image is a challenging problem, especially under partial occlusions and extreme poses. This is because the uncertainty of the estimated 2D landmarks will affect the quality of face reconstruction. In this paper, we propose a novel joint 2D and 3D optimization method to adaptively reconstruct 3D face shapes from a single image, which combines the depths of 3D landmarks to solve the uncertain detections of invisible landmarks. The strategy of our method involves two aspects: a coarse-to-fine pose estimation using both 2D and 3D landmarks, and an adaptive 2D and 3D re-weighting based on the refined pose parameter to recover accurate 3D faces. Experimental results on multiple datasets demonstrate that our method can generate high-quality reconstruction from a single color image and is robust for self-occlusion and large poses.

The paper "Adaptive 3D Face Reconstruction from a Single Image" addresses the complex challenge of reconstructing 3D face models from a single image. This task is particularly difficult due to issues such as partial occlusions and extreme head poses, which can lead to uncertainty in the position of 2D landmarks, thereby affecting the accuracy of the 3D reconstruction.

The authors propose a novel joint 2D and 3D optimization approach. This method strategically combines the depth information of 3D landmarks to tackle the problem of uncertain detections of invisible landmarks, which are those not readily visible in the image due to occlusion or certain viewing angles.

The reconstruction process involves two main strategies:

1. Coarse-to-Fine Pose Estimation:
   • The method utilizes both 2D and 3D landmarks to perform a multistage pose estimation process. This allows for an initial crude alignment that gradually becomes more refined, improving the accuracy of the reconstructed face shape.
2. Adaptive Re-weighting:
   • The approach employs an adaptive re-weighting scheme to adjust the influence of both 2D and 3D data based on the pose parameters. This adaptation is crucial to refining the reconstruction by prioritizing more reliable landmark data, thus addressing the uncertainties inherent in single-image inputs.

The results, validated across several datasets, demonstrate the method's ability to produce high-quality 3D face models. The approach is shown to be robust in scenarios involving self-occlusion and large pose variations, making it a significant contribution to the field of 3D face reconstruction.

This paper emphasizes integrating depth information at the landmark level and adapting the reconstruction strategy according to the pose, which together enhance the robustness and precision of the reconstruction from singular, potentially challenging images.