2 citations · 2 across the 4 of their papers we have counts for
6 papers
VolHuMe: a High-Resolution Large Scale Dataset of Volumetric Human Meshes
Giulia Martinelli, Niccolò Bisagno, Nicola Garau +2
We introduce VolHuMe, a dataset of high-quality 4D human scans captured with a state-of-the-art volumetric studio using 64 RGB and 32 depth cameras. VolHuMe contains individual cap…
SDFoam: Signed-Distance Foam for explicit surface reconstruction
Antonella Rech, Nicola Conci, Nicola Garau
Neural radiance fields (NeRF) have driven impressive progress in view synthesis by using ray-traced volumetric rendering. Splatting-based methods such as 3D Gaussian Splatting (3DG…
EPOCH: Jointly Estimating the 3D Pose of Cameras and Humans
Nicola Garau, Giulia Martinelli, Niccolò Bisagno +2
Monocular Human Pose Estimation (HPE) aims at determining the 3D positions of human joints from a single 2D image captured by a camera. However, a single 2D point in the image may…
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis
Niccolò Bisagno, Nicola Garau, Antonio Luigi Stefani +1
Simulation is a powerful tool to easily generate annotated data, and a highly desirable feature, especially in those domains where learning models need large training datasets. Mac…
Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks
Nicola Garau, Niccolò Bisagno, Zeno Sambugaro +1
Deep neural networks achieve outstanding results in a large variety of tasks, often outperforming human experts. However, a known limitation of current neural architectures is the…
DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders
Nicola Garau, Niccolò Bisagno, Piotr Bródka +1
Human Pose Estimation (HPE) aims at retrieving the 3D position of human joints from images or videos. We show that current 3D HPE methods suffer a lack of viewpoint equivariance, n…