Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures
This addresses the challenge of unregistered meshes for researchers in 3D computer vision, though it is incremental as it builds on existing geometric measure methods.
The paper tackles the problem of inconsistent 3D data in generative modeling by proposing an architecture with a kernel-based loss function using geometric measures, enabling mesh-invariant training and achieving efficient and resilient results in human face generation.
3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. However, the acquired data is often inconsistent, resulting in unregistered meshes or point clouds. Many generative learning algorithms require correspondence between each point when comparing the predicted shape and the target shape. We propose an architecture able to cope with different parameterizations, even during the training phase. In particular, our loss function is built upon a kernel-based metric over a representation of meshes using geometric measures such as currents and varifolds. The latter allows to implement an efficient dissimilarity measure with many desirable properties such as robustness to resampling of the mesh or point cloud. We demonstrate the efficiency and resilience of our model with a generative learning task of human faces.