Search Results for author:
Found 9 papers, 5 papers with code
Creating a Digital Twin of Spinal Surgery: A Proof of Concept
In this paper, we present a proof of concept (PoC) for surgery digitalization that is applied to an ex-vivo spinal surgery performed in realistic conditions.
Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
To the best of our knowledge, our proposed framework is the first diffusion model to enable the creation of fully 3D-consistent, animatable, and photorealistic human avatars from a single image of an unseen subject; extensive quantitative and qualitative evaluations demonstrate the advantages of our approach over existing state-of-the-art avatar creation models on both novel view and novel expression synthesis tasks.
ResFields: Residual Neural Fields for Spatiotemporal Signals
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, such as signed distance (SDFs) or radiance fields (NeRFs), via a single multi-layer perceptron (MLP).
Dynamic Point Fields
In this work, we present a dynamic point field model that combines the representational benefits of explicit point-based graphics with implicit deformation networks to allow efficient modeling of non-rigid 3D surfaces.
HARP: Personalized Hand Reconstruction from a Monocular RGB Video
We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstructs a faithful hand avatar exhibiting a high-fidelity appearance and geometry.
SMPLpix: Neural Avatars from 3D Human Models
Recent advances in deep generative models have led to an unprecedented level of realism for synthetically generated images of humans.
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Our method uses encoder and decoder deep networks that maps complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations.
Efficient Learning on Point Clouds with Basis Point Sets
The basis point set representation is a residual representation that can be computed efficiently and can be used with standard neural network architectures and other machine learning algorithms.
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy.
