15 citations · 16 across the 5 of their papers we have counts for
5 papers
HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs
Artem Sevastopolsky, Philip-William Grassal, Simon Giebenhain +3
Current advances in human head modeling allow the generation of plausible-looking 3D head models via neural representations, such as NeRFs and SDFs. Nevertheless, constructing comp…
Neural Head Avatars from Monocular RGB Videos
Philip-William Grassal, Malte Prinzler, Titus Leistner +3
We present Neural Head Avatars, a novel neural representation that explicitly models the surface geometry and appearance of an animatable human avatar that can be used for teleconf…
Quantifying identifiability to choose and audit in differentially private deep learning
Daniel Bernau, Günther Eibl, Philip W. Grassal +2
Differential privacy allows bounding the influence that training data records have on a machine learning model. To use differential privacy in machine learning, data scientists mus…
Assessing differentially private deep learning with Membership Inference
Daniel Bernau, Philip-William Grassal, Jonas Robl +1
Attacks that aim to identify the training data of public neural networks represent a severe threat to the privacy of individuals participating in the training data set. A possible…
The Influence of Differential Privacy on Short Term Electric Load Forecasting
Günther Eibl, Kaibin Bao, Philip-William Grassal +2
There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to bi…