We propose a novel localized Neural Field (LoVD), the first self-supervised task for tuning neural fields (INT), and an efficient (takes less than a minute) scalable registration pipeline (NSR), that works with out-of-distribution data (partial point clouds, clutter, different poses, …).
Given the wearable sensors and the interactive environment, we want to estimate human and object positions and achieve visually plausible results.
We propose a fine-grained dataset for 3D human clothing segmentation (CloSe-D), the first learning-based 3D clothing segmentation model (CloSe-Net), and an interactive tool for refining 3D segmentation labels (CloSe-T)
The Neural Surface Fields (NSF) defines a Neural Field on the level set of an implicit representation, providing a continuous and flexible function representation on 3D geometries. We apply it in an avatarization pipeline, learning animatable avatars with pose-dependent deformations starting from a sparse set of partial depth views.
We show that an unorganized 3D human point cloud provides enough information to infer a 3D interacted object, opening new directions in the human-object interaction research. We also analyze the impact of different levels of information and a saliency study about the geometrical features of the input human point cloud.
We propose a parallel decoding algorithm to speedup transformer inference for translation.
A novel approach for non-rigid correspondence based on mesh-free methods.
We combine the spectra of different linear operators to learn of to semantically modify shape geometries.
We propose a novel characterization of the mold algorithm to work on arbitrary curved surfaces.
A new representation for skeleton regressors, and an efficient transfer via Laplacian eigenfunctions.
A data-driven solution and analysis of the theoretical problem of recovering a shape from its spectrum
We propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds.
Inspired by the Functional Maps paradigma, we learn a linearly-invariant embedding for 3D Shape Matching
A full automatic pipeline for 3D human shapes that combine intrinsic shape matching, automatic landmarks detection, and template registration