2 citations · 4 across the 6 of their papers we have counts for
7 papers
Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction
Cecilia Curreli, Dominik Muhle, Abhishek Saroha +3
Probabilistic human motion prediction aims to forecast multiple possible future movements from past observations. While current approaches report high diversity and realism, they o…
Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo
Mohammed Brahimi, Bjoern Haefner, Zhenzhang Ye +2
Neural approaches have shown a significant progress on camera-based reconstruction. But they require either a fairly dense sampling of the viewing sphere, or pre-training on an exi…
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
Zhenzhang Ye, Gabriel Peyré, Daniel Cremers +1
Bilevel optimization aims to optimize an outer objective function that depends on the solution to an inner optimization problem. It is routinely used in Machine Learning, notably f…
Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections
Zhenzhang Ye, Tarun Yenamandra, Florian Bernard +1
Graph matching aims to establish correspondences between vertices of graphs such that both the node and edge attributes agree. Various learning-based methods were recently proposed…
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning
Zhenzhang Ye, Thomas Möllenhoff, Tao Wu +1
Structured convex optimization on weighted graphs finds numerous applications in machine learning and computer vision. In this work, we propose a novel adaptive preconditioning str…
Variational Uncalibrated Photometric Stereo under General Lighting
Bjoern Haefner, Zhenzhang Ye, Maolin Gao +3
Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable. To eliminate such restrictions,…
Combinatorial Preconditioners for Proximal Algorithms on Graphs
Thomas Möllenhoff, Zhenzhang Ye, Tao Wu +1
We present a novel preconditioning technique for proximal optimization methods that relies on graph algorithms to construct effective preconditioners. Such combinatorial preconditi…