Disjoint Pose and Shape for 3D Face Reconstruction
Raja Kumar Jiahao Luo Alex Pang James Davis
Existing methods for 3D face reconstruction from a few casually captured
images employ deep learning based models along with a 3D Morphable Model(3DMM)
as face geometry prior. Structure From Motion(SFM), followed by Multi-View
Stereo (MVS), on the other hand, uses dozens of high-resolution images to
reconstruct accurate 3D faces.However, it produces noisy and stretched-out
results with only two views available. In this paper, taking inspiration from
both these methods, we propose an end-to-end pipeline that disjointly solves
for pose and shape to make the optimization stable and accurate. We use a face
shape prior to estimate face pose and use stereo matching followed by a 3DMM to
solve for the shape. The proposed method achieves end-to-end topological
consistency, enables iterative face pose refinement procedure, and show
remarkable improvement on both quantitative and qualitative results over
existing state-of-the-art methods.