no code implementations • 27 Mar 2024 • Ruikai Cui, Weizhe Liu, Weixuan Sun, Senbo Wang, Taizhang Shang, Yang Li, Xibin Song, Han Yan, Zhennan Wu, Shenzhou Chen, Hongdong Li, Pan Ji
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints.
no code implementations • 24 Mar 2024 • Han Yan, Yang Li, Zhennan Wu, Shenzhou Chen, Weixuan Sun, Taizhang Shang, Weizhe Liu, Tian Chen, Xiaqiang Dai, Chao Ma, Hongdong Li, Pan Ji
We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass.
no code implementations • 30 Jan 2024 • Zhennan Wu, Yang Li, Han Yan, Taizhang Shang, Weixuan Sun, Senbo Wang, Ruikai Cui, Weizhe Liu, Hiroyuki Sato, Hongdong Li, Pan Ji
A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed.
1 code implementation • 19 Sep 2023 • Jiaxin Wei, Xibin Song, Weizhe Liu, Laurent Kneip, Hongdong Li, Pan Ji
While showing promising results, recent RGB-D camera-based category-level object pose estimation methods have restricted applications due to the heavy reliance on depth sensors.
1 code implementation • 19 Oct 2022 • Martin Engilberge, Weizhe Liu, Pascal Fua
Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes.
Ranked #2 on Multi-Object Tracking on Wildtrack
no code implementations • CVPR 2022 • Weizhe Liu, Bugra Tekin, Huseyin Coskun, Vibhav Vineet, Pascal Fua, Marc Pollefeys
To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions.
no code implementations • 22 Apr 2021 • Weizhe Liu, David Ferstl, Samuel Schulter, Lukas Zebedin, Pascal Fua, Christian Leistner
We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation.
1 code implementation • CVPR 2022 • Weizhe Liu, Nikita Durasov, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
1 code implementation • 1 Dec 2020 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images.
no code implementations • 26 Nov 2019 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
1 code implementation • ECCV 2020 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing.
3 code implementations • CVPR 2019 • Weizhe Liu, Mathieu Salzmann, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
Ranked #1 on Crowd Counting on Venice
no code implementations • 23 Mar 2018 • Weizhe Liu, Krzysztof Lis, Mathieu Salzmann, Pascal Fua
In this paper, we explicitly model the scale changes and reason in terms of people per square-meter.