@conference {cRamonb, title = {H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2021}, publisher = {IEEE/CVF}, organization = {IEEE/CVF}, address = {Virtual}, abstract = {

Recent learning approaches that implicitly represent surface geometry using coordinate-based neural representations have shown impressive results in the problem of multi-view 3D reconstruction. The effectiveness of these techniques is, however, subject to the availability of a large number (several tens) of input views of the scene, and computationally demanding optimizations. In this paper, we tackle these limitations for the specific problem of few-shot full 3D head reconstruction, by endowing coordinate-based representations with a probabilistic shape prior that enables faster convergence and better generalization when using few input images (down to three). First, we learn a shape model of 3D heads from thousands of incomplete raw scans using implicit representations. At test time, we jointly overfit two coordinate-based neural networks to the scene, one modeling the geometry and another estimating the surface radiance, using implicit differentiable rendering. We devise a two-stage optimization strategy in which the learned prior is used to initialize and constrain the geometry during an initial optimization phase. Then, the prior is unfrozen and fine-tuned to the scene. By doing this, we achieve high-fidelity head reconstructions, including hair and shoulders, and with a high level of detail that consistently outperforms both state-of-the-art 3D Morphable Models methods in the few-shot scenario, and non-parametric methods when large sets of views are available.

}, author = {Ramon, Eduard and Triginer, Gil and Escur, Janna and Pumarola, Albert and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto and Moreno, Francesc} } @conference {cRamon, title = {Hyperparameter-Free Losses for Model-Based Monocular Reconstruction}, booktitle = {ICCV 2019 Workshop on Geometry Meets Deep Learning}, year = {2019}, month = {11/2019}, publisher = {IEEE / Computer Vision Foundation}, organization = {IEEE / Computer Vision Foundation}, address = {Seoul, South Corea}, abstract = {

This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the object and the camera pose are jointly optimized in a sole term expression. This simplification reduces the optimization time and its complexity. Moreover, we propose a novel implicit regularization technique based on random virtual projections that does not require additional 2D or 3D annotations. Our experiments suggest that minimizing a shape reprojection error together with the proposed implicit regularization is especially suitable for applications that require precise alignment between geometry and image spaces, such as augmented reality. We evaluate our losses on a large scale dataset with 3D ground truth and publish our implementations to facilitate reproducibility and public benchmarking in this field.

}, author = {Ramon, Eduard and Ruiz, Guillermo and Batard, Thomas and Xavier Gir{\'o}-i-Nieto} }