Designing realistic digital humans is extremely complex. Most data‐driven generative
models used to simplify the creation of their underlying geometric shape do not offer control …

Abstract We present the Locally Adaptive Morphable Model (LAMM) a highly flexible Auto-
Encoder (AE) framework for learning to generate and manipulate 3D meshes. We train our …

Standard registration algorithms need to be independently applied to each surface to
register, following careful pre-processing and hand-tuning. Recently, learning-based …

Mesh is a type of data structure commonly used for 3-D shapes. Representation learning for
3-D meshes is essential in many computer vision and graphics applications. The recent …

3D reverse engineering is a long sought-after, yet not completely achieved goal in the
Computer-Aided Design (CAD) industry. The objective is to recover the construction history …

Deviation from a normal facial shape and symmetry can arise from numerous sources,
including physical injury and congenital birth defects. Such abnormalities can have …

Equivariance to symmetries has proven to be a powerful inductive bias in deep learning
research. Recent works on mesh processing have concentrated on various kinds of natural …

Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies
have shown that artificial intelligence (AI) based facial analysis can match the diagnostic …

Parametric 3D body models like SMPL only represent minimally-clothed people and are
hard to extend to clothing because they have a fixed mesh topology and resolution. To …

The success of deep learning models on structured data has generated significant interest in
extending their application to non-Euclidean domains. In this work, we introduce a novel …