6 papers
Neural Motion Blending Across Arbitrary Character Topologies
Luca Cazzola, Giulia Martinelli, Nicola Conci
Motion blending in character animation enables the synthesis of new motions by interpolating between existing examples. Current methods are typically restricted to fixed skeleton t…
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…
Meta-Learning Transformers to Improve In-Context Generalization
Lorenzo Braccaioli, Anna Vettoruzzo, Prabhant Singh +3
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms t…
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…
Signal Processing for Haptic Surface Modeling: a Review
Antonio Luigi Stefani, Niccolò Bisagno, Andrea Rosani +2
Haptic feedback has been integrated into Virtual and Augmented Reality, complementing acoustic and visual information and contributing to an all-round immersive experience in multi…
Lagrangian Hashing for Compressed Neural Field Representations
Shrisudhan Govindarajan, Zeno Sambugaro, Akhmedkhan +7
We present Lagrangian Hashing, a representation for neural fields combining the characteristics of fast training NeRF methods that rely on Eulerian grids (i.e.~InstantNGP), with th…