no code implementations • 8 Apr 2024 • Dingxi Zhang, Zhuoxun Chen, Yu-Jie Yuan, Fang-Lue Zhang, Zhenliang He, Shiguang Shan, Lin Gao
With the rapid development of XR, 3D generation and editing are becoming more and more important, among which, stylization is an important tool of 3D appearance editing.
no code implementations • 18 Feb 2024 • Yup-Jiang Dong, Fang-Lue Zhang, Song-Hai Zhang
To address this issue, we present Motion-Aware Loss, which leverages the temporal relation among consecutive input frames and a novel distillation scheme between the teacher and student networks in the multi-frame self-supervised depth estimation methods.
no code implementations • 20 Dec 2023 • Yue-Jiang Dong, Yuan-Chen Guo, Ying-Tian Liu, Fang-Lue Zhang, Song-Hai Zhang
Self-supervised monocular depth estimation is of significant importance with applications spanning across autonomous driving and robotics.
no code implementations • 27 Jul 2022 • Yiheng Li, Connelly Barnes, Kun Huang, Fang-Lue Zhang
Optical flow computation is essential in the early stages of the video processing pipeline.
no code implementations • 31 Mar 2022 • Rongsen Chen, Fang-Lue Zhang, Simon Finnie, Andrew Chalmers, Teahyun Rhee
Six degrees-of-freedom (6-DoF) video provides telepresence by enabling users to move around in the captured scene with a wide field of regard.
1 code implementation • 2 Oct 2020 • Rao Fu, Jie Yang, Jiawei Sun, Fang-Lue Zhang, Yu-Kun Lai, Lin Gao
Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures.
no code implementations • 14 Apr 2020 • Xian Wu, Xiao-Nan Fang, Tao Chen, Fang-Lue Zhang
We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images.
no code implementations • 24 Mar 2020 • Min Shi, Jia-Qi Zhang, Shu-Yu Chen, Lin Gao, Yu-Kun Lai, Fang-Lue Zhang
The color transform network takes the target line art images as well as the line art and color images of one or more reference images as input, and generates corresponding target color images.
no code implementations • 19 Feb 2020 • Yun-Peng Xiao, Yu-Kun Lai, Fang-Lue Zhang, Chunpeng Li, Lin Gao
However, the performance for different applications largely depends on the representation used, and there is no unique representation that works well for all applications.
Graphics
no code implementations • 23 Aug 2018 • Xian Wu, Rui-Long Li, Fang-Lue Zhang, Jian-Cheng Liu, Jue Wang, Ariel Shamir, Shi-Min Hu
We evaluate our method on public portrait image datasets, and show that it outperforms other state-of-art general image completion methods.
Graphics