1 code implementation • 31 Mar 2024 • Jihua Peng, Yanghong Zhou, P. Y. Mok
This paper presents a novel Kinematics and Trajectory Prior Knowledge-Enhanced Transformer (KTPFormer), which overcomes the weakness in existing transformer-based methods for 3D human pose estimation that the derivation of Q, K, V vectors in their self-attention mechanisms are all based on simple linear mapping.
Ranked #2 on 3D Human Pose Estimation on MPI-INF-3DHP
1 code implementation • 15 Aug 2023 • Zhengwentai Sun, Yanghong Zhou, HongHong He, P. Y. Mok
This paper reports on the development of \textbf{a novel style guided diffusion model (SGDiff)} which overcomes certain weaknesses inherent in existing models for image synthesis.
no code implementations • 6 Jun 2023 • Yujuan Ding, Zhihui Lai, P. Y. Mok, Tat-Seng Chua
Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years.
no code implementations • 17 Apr 2023 • Hao Tian, Yu Cao, P. Y. Mok
Clothing segmentation and fine-grained attribute recognition are challenging tasks at the crossing of computer vision and fashion, which segment the entire ensemble clothing instances as well as recognize detailed attributes of the clothing products from any input human images.
1 code implementation • 20 Mar 2023 • Yu Cao, Xiangqiao Meng, P. Y. Mok, Xueting Liu, Tong-Yee Lee, Ping Li
Through multiple quantitative metrics evaluated on our dataset and a user study, we demonstrate AnimeDiffusion outperforms state-of-the-art GANs-based models for anime face line drawing colorization.
1 code implementation • 21 Dec 2022 • Yu Cao, Hao Tian, P. Y. Mok
Automatic colorization of anime line drawing has attracted much attention in recent years since it can substantially benefit the animation industry.
no code implementations • 7 Mar 2022 • Yujuan Ding, P. Y. Mok, Xun Yang, Yanghong Zhou
Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones.