Papers › DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction
DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction
Xiaoxing Zeng, Xiaojiang Peng, Yu Qiao
Reconstructing the detailed geometric structure from a single face image is a challenging problem due to its ill-posed nature and the fine 3D structures to be recovered. This paper proposes a deep Dense-Fine-Finer Network (DF2Net) to address this challenging problem. DF2Net decomposes the reconstruction process into three stages, each of which is processed by an elaborately-designed network, namely D-Net, F-Net, and Fr-Net. D-Net exploits a U-net architecture to map the input image to a dense depth image. F-Net refines the output of D-Net by integrating features from depth and RGB domains, whose output is further enhanced by Fr-Net with a novel multi-resolution hypercolumn architecture. In addition, we introduce three types of data to train these networks, including 3D model synthetic data, 2D image reconstructed data, and fine facial images. We elaborately exploit different datasets (or combination) together with well-designed losses to train different networks. Qualitative evaluation indicates that our DF2Net can effectively reconstruct subtle facial details such as small crow's feet and wrinkles. Our DF2Net achieves performance superior or comparable to state-of-the-art algorithms in qualitative and quantitative analyses on real-world images and the BU-3DFE dataset. Code and the collected 70K image-depth data will be publicly available.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections