TEMPEH: Instant Multi-View Head Capture through Learnable Registration (CVPR 2023)

8 May 2023

05:49

Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow, and commonly address the problem in two separate steps; multi-view stereo (MVS) reconstruction followed by non-rigid registration. To simplify this process, we introduce TEMPEH (Towards Estimation of 3D Meshes from Performances of Expressive Heads) to directly infer 3D heads in dense correspondence from calibrated multi-view images. Registering datasets of 3D scans typically requires manual parameter tuning to find the right balance between accurately fitting the scans’ surfaces and being robust to scanning noise and outliers. Instead, we propose to jointly register a 3D head dataset while training TEMPEH. Specifically, during training we minimize a geometric loss commonly used for surface registration, effectively leveraging TEMPEH as a regularizer. Our multi-view head inference builds on a volumetric feature representation that samples and fuses features from each view using camera calibration information. To account for partial occlusions and a large capture volume that enables head movements, we use view- and surface-aware feature fusion, and a spatial transformer-based head localization module, respectively. We use raw MVS scans as supervision during training, but, once trained, TEMPEH directly predicts 3D heads in dense correspondence without requiring scans. Predicting one head takes about 0.3 seconds with a median reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art. This enables the efficient capture of large datasets containing multiple people and diverse facial motions. Code, model, and data are publicly available. FaMoS dataset: FaMoS is a dynamic 3D head dataset from 95 subjects, each performing 28 motion sequences. The sequences comprise of six prototypical expressions (i.e., Anger, Disgust, Fear, Happiness, Sadness, and Surprise), two head rotations (left/right and up/down), and diverse facial motions, including extreme and asymmetric expressions. Each sequence is recorded at 60 fps. In total, FaMoS contains around 600K 3D head meshes (i.e., ~225 frames per sequence). For each frame, we compute a registration in FLAME mesh topology, which are downloadable here for research purposes. You must sign up and agree to the license to download the data. Project: https://tempeh.is.tue.mpg.de/ Paper: https://ps.is.mpg.de/uploads_file/attachment/attachment/711/CVPR2023_Multiview_Face_Capture.pdf Dataset: https://tempeh.is.tue.mpg.de/#dataset Citation: @inproceedings{TMPEH:CVPR:2023, title = {Instant Multi-View Head Capture through Learnable Registration}, author = {Bolkart, Timo and Li, Tianye and Black, Michael J.}, booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2023} }