26 citations · 52 across the 11 of their papers we have counts for
5 papers · 1 filter
Representing 3D Shapes with Probabilistic Directed Distance Fields
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks.…
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks
Vineeth S. Bhaskara, Tristan Aumentado-Armstrong, Allan Jepson +1
Modern generative adversarial networks (GANs) predominantly use piecewise linear activation functions in discriminators (or critics), including ReLU and LeakyReLU. Such models lear…
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis +2
In this work, we consider the problem of sequence-to-sequence alignment for signals containing outliers. Assuming the absence of outliers, the standard Dynamic Time Warping (DTW) a…
Representation Learning via Global Temporal Alignment and Cycle-Consistency
Isma Hadji, Konstantinos G. Derpanis, Allan D. Jepson
We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is t…
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
A complete representation of 3D objects requires characterizing the space of deformations in an interpretable manner, from articulations of a single instance to changes in shape ac…