no code implementations • 18 Mar 2024 • Sibi Catley-Chandar, Richard Shaw, Gregory Slabaugh, Eduardo Perez-Pellitero
Conversely, existing 3D enhancers are able to transfer detail from nearby training images in a generalizable manner, but suffer from inaccurate camera calibration and can propagate errors from the geometry into rendered images.
no code implementations • 22 Dec 2023 • HyunJun Jung, Nikolas Brasch, Jifei Song, Eduardo Perez-Pellitero, Yiren Zhou, Zhihao LI, Nassir Navab, Benjamin Busam
ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar.
no code implementations • 20 Dec 2023 • Richard Shaw, Jifei Song, Arthur Moreau, Michal Nazarczuk, Sibi Catley-Chandar, Helisa Dhamo, Eduardo Perez-Pellitero
We model the dynamics of a scene using a tunable MLP, which learns the deformation field from a canonical space to a set of 3D Gaussians per frame.
no code implementations • 28 Mar 2022 • Richard Shaw, Sibi Catley-Chandar, Ales Leonardis, Eduardo Perez-Pellitero
Our proposed approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the HdM HDR dataset, respectively.
no code implementations • CVPR 2016 • Eduardo Perez-Pellitero, Jordi Salvador, Javier Ruiz-Hidalgo, Bodo Rosenhahn
The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results.
no code implementations • ICCV 2015 • Jordi Salvador, Eduardo Perez-Pellitero
This paper presents a fast, high-performance method for super resolution with external learning.