no code implementations • 27 Jun 2024 • Chirui Chang, Zhengzhe Liu, Xiaoyang Lyu, Xiaojuan Qi
In this study, we examine this gap from three fundamental perspectives: appearance, motion, and geometry, comparing real-world videos with those generated by a state-of-the-art AI model, Stable Video Diffusion.
no code implementations • 11 Jun 2024 • Zhengzhe Liu, Qing Liu, Chirui Chang, Jianming Zhang, Daniil Pakhomov, Haitian Zheng, Zhe Lin, Daniel Cohen-Or, Chi-Wing Fu
Deoccluding the hidden portions of objects in a scene is a formidable task, particularly when addressing real-world scenes.
no code implementations • 4 Feb 2024 • Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, Chi-Wing Fu
First, we design the coupled neural shape (CNS) representation for supporting 3D shape editing.
1 code implementation • 20 Jan 2024 • Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, Chi-Wing Fu
Further, we derive the subband adaptive training strategy to train our model to effectively learn to generate coarse and detail wavelet coefficients.
1 code implementation • 3 Nov 2023 • Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui, Xiaojuan Qi, Daniel Cohen-Or, Chi-Wing Fu
This paper presents a new text-guided technique for generating 3D shapes.
1 code implementation • ICCV 2023 • Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu, Xiaojuan Qi
In this work, we focus on synthesizing high-quality textures on 3D meshes.
no code implementations • 14 Jun 2023 • Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, Chi-Wing Fu
This paper presents CLIPXPlore, a new framework that leverages a vision-language model to guide the exploration of the 3D shape space.
1 code implementation • 26 Mar 2023 • Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
3D scene understanding, e. g., point cloud semantic and instance segmentation, often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare.
2 code implementations • 24 Mar 2023 • Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu
The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text.
no code implementations • 1 Feb 2023 • Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Ruihui Li, Chi-Wing Fu
This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain.
no code implementations • CVPR 2023 • Ruihang Chu, Zhengzhe Liu, Xiaoqing Ye, Xiao Tan, Xiaojuan Qi, Chi-Wing Fu, Jiaya Jia
The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations.
1 code implementation • 28 Nov 2022 • Yingxian Chen, Zhengzhe Liu, Baoheng Zhang, Wilton Fok, Xiaojuan Qi, Yik-Chung Wu
Weakly supervised detection of anomalies in surveillance videos is a challenging task.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
1 code implementation • 23 Nov 2022 • Tianyu Wang, Xiaowei Hu, Zhengzhe Liu, Chi-Wing Fu
Importantly, we formulate the lightweight plug-in S2D module and the point cloud reconstruction module in SDet to densify 3D features and train SDet to produce 3D features, following the dense 3D features in DDet.
2 code implementations • 9 Sep 2022 • Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu
Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes.
1 code implementation • CVPR 2022 • Zhengzhe Liu, Yi Wang, Xiaojuan Qi, Chi-Wing Fu
In this work, we explore the challenging task of generating 3D shapes from text.
no code implementations • CVPR 2022 • Ruihang Chu, Xiaoqing Ye, Zhengzhe Liu, Xiao Tan, Xiaojuan Qi, Chi-Wing Fu, Jiaya Jia
We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation.
1 code implementation • CVPR 2021 • Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training, so the 2D network can infer without requiring 3D data.
2 code implementations • CVPR 2021 • Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
Point cloud semantic segmentation often requires largescale annotated training data, but clearly, point-wise labels are too tedious to prepare.
2 code implementations • 13 Dec 2020 • Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip H. S. Torr, Raquel Urtasun, Jiaya Jia
Note that GeoNet++ is generic and can be used in other depth/normal prediction frameworks to improve the quality of 3D reconstruction and pixel-wise accuracy of depth and surface normals.
1 code implementation • CVPR 2020 • Zhengzhe Liu, Xiaojuan Qi, Philip Torr
In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets.
1 code implementation • CVPR 2018 • Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, Jiaya Jia
In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image.