Graph Refinement With Regression Prior For 3D Face Reconstruction

Recent 3D face reconstruction methods show encouraging results in retrieving 3D face shape and texture from monocular face images. However, these approaches pose several dependencies during testing, such as the requirement of facial landmark coordinates. Moreover, a large testing time presents a challenge for real-time applications. To address these issues, we propose REduced Dependency Fast UnsuperviSEd 3D Face Reconstruction (RED-FUSE) framework, which uses unprocessed face images to estimate reliable 3D face shape and texture, thus eliminating the need for prior landmark knowledge, and considerable prediction time during testing. RED-FUSE outperforms the current state-of-the-art method on CelebA dataset e.g., for 3D shape and color-based errors, a reduction from 5.84 � 0.16 to 3.14 � 0.11 and from 3.50 � 0.14 to 2.97 � 0.09 is observed, leading to an improvement of 46.2% and 15.1%, respectively. in addition, the testing time reduces from 7.30 msec to 1.85 msec per face, showing the effectiveness of our method.