activity
20152022
most citedTLIO: Tight Learned Inertial Odometry

225 citations · 580 across the 19 of their papers we have counts for

collaborators

42 papers

cs.CV20221 cited

Probabilistic Shape Completion by Estimating Canonical Factors with Hierarchical VAE

Wen Jiang, Kostas Daniilidis

We propose a novel method for 3D shape completion from a partial observation of a point cloud. Existing methods either operate on a global latent code, which limits the expressiven…

cs.CV20222 cited

CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphism

Jiahui Lei, Kostas Daniilidis

While neural representations for static 3D shapes are widely studied, representations for deformable surfaces are limited to be template-dependent or lack efficiency. We introduce…

cs.CV2021

Probabilistic Modeling for Human Mesh Recovery

Nikos Kolotouros, Georgios Pavlakos, Dinesh Jayaraman +1

This paper focuses on the problem of 3D human reconstruction from 2D evidence. Although this is an inherently ambiguous problem, the majority of recent works avoid the uncertainty…

cs.CV20211 cited

Deformable Linear Object Prediction Using Locally Linear Latent Dynamics

Wenbo Zhang, Karl Schmeckpeper, Pratik Chaudhari +1

We propose a framework for deformable linear object prediction. Prediction of deformable objects (e.g., rope) is challenging due to their non-linear dynamics and infinite-dimension…

cs.CV20201 cited

Learning Portrait Style Representations

Sadat Shaik, Bernadette Bucher, Nephele Agrafiotis +3

Style analysis of artwork in computer vision predominantly focuses on achieving results in target image generation through optimizing understanding of low level style characteristi…

cs.AI2020

Joint Estimation of Image Representations and their Lie Invariants

Christine Allen-Blanchette, Kostas Daniilidis

Images encode both the state of the world and its content. The former is useful for tasks such as planning and control, and the latter for classification. The automatic extraction…