1 code implementation • 14 Oct 2024 • Runsong Zhu, Shi Qiu, Qianyi Wu, Ka-Hei Hui, Pheng-Ann Heng, Chi-Wing Fu
Panoptic lifting is an effective technique to address the 3D panoptic segmentation task by unprojecting 2D panoptic segmentations from multi-views to 3D scene.
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.
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.
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.
1 code implementation • 19 Sep 2022 • Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu
This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain.
no code implementations • 14 Jun 2022 • Runsong Zhu, Di Kang, Ka-Hei Hui, Yue Qian, Xuefei Zhe, Zhen Dong, Linchao Bao, Pheng-Ann Heng, Chi-Wing Fu
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
1 code implementation • CVPR 2022 • Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology.
1 code implementation • 10 Aug 2021 • Ruihui Li, Xianzhi Li, Ka-Hei Hui, Chi-Wing Fu
We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds.