Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Kania, Kacper"'
Decoupling lighting from geometry using unconstrained photo collections is notoriously challenging. Solving it would benefit many users, as creating complex 3D assets takes days of manual labor. Many previous works have attempted to address this issu
Externí odkaz:
http://arxiv.org/abs/2408.04474
Autor:
Esposito, Salvatore, Xu, Qingshan, Kania, Kacper, Hewitt, Charlie, Mariotti, Octave, Petikam, Lohit, Valentin, Julien, Onken, Arno, Mac Aodha, Oisin
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. Most existing approaches predict volumetric density to render multi-view consistent images. By employing volumetric rendering using neural ra
Externí odkaz:
http://arxiv.org/abs/2406.04254
Autor:
Xia, Shaobo, Yue, Jun, Kania, Kacper, Fang, Leyuan, Tagliasacchi, Andrea, Yi, Kwang Moo, Sun, Weiwei
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations while achieving the performance of recent fully supervised approaches. Our core idea is to propagate the
Externí odkaz:
http://arxiv.org/abs/2312.06799
Autor:
Kania, Kacper, Garbin, Stephan J., Tagliasacchi, Andrea, Estellers, Virginia, Yi, Kwang Moo, Valentin, Julien, Trzciński, Tomasz, Kowalski, Marek
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the
Externí odkaz:
http://arxiv.org/abs/2305.07514
We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask
Externí odkaz:
http://arxiv.org/abs/2112.01983
The creation of plausible and controllable 3D human motion animations is a long-standing problem that requires a manual intervention of skilled artists. Current machine learning approaches can semi-automate the process, however, they are limited in a
Externí odkaz:
http://arxiv.org/abs/2104.00351
Autor:
Spurek, Przemysław, Winczowski, Sebastian, Zięba, Maciej, Trzciński, Tomasz, Kania, Kacper, Mazur, Marcin
Recently proposed 3D object reconstruction methods represent a mesh with an atlas - a set of planar patches approximating the surface. However, their application in a real-world scenario is limited since the surfaces of reconstructed objects contain
Externí odkaz:
http://arxiv.org/abs/2102.05984
Autor:
Stypułkowski, Michał, Kania, Kacper, Zamorski, Maciej, Zięba, Maciej, Trzciński, Tomasz, Chorowski, Jan
In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation matches intrinsic characteristics of point clouds: the number of points and
Externí odkaz:
http://arxiv.org/abs/2010.11087
Signed distance field (SDF) is a prominent implicit representation of 3D meshes. Methods that are based on such representation achieved state-of-the-art 3D shape reconstruction quality. However, these methods struggle to reconstruct non-convex shapes
Externí odkaz:
http://arxiv.org/abs/2006.09102
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask