no code implementations • CVPR 2023 • Ziwei Yu, Chen Li, Linlin Yang, Xiaoxu Zheng, Michael Bi Mi, Gim Hee Lee, Angela Yao
However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes.
1 code implementation • 29 Apr 2023 • Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, Angela Yao
Video super-resolution commonly uses a frame-wise alignment to support the propagation of information over time.
Ranked #1 on
Video Super-Resolution
on REDS4- 4x upscaling
1 code implementation • CVPR 2023 • Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, Xinchao Wang
Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks.
no code implementations • 25 Jan 2023 • Kerui Gu, Linlin Yang, Michael Bi Mi, Angela Yao
Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.
1 code implementation • 21 Jan 2023 • Shihao Zhang, Linlin Yang, Michael Bi Mi, Xiaoxu Zheng, Angela Yao
In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance.
Ranked #9 on
Monocular Depth Estimation
on NYU-Depth V2
1 code implementation • 24 Nov 2022 • Xin Yang, Michael Bi Mi, Yuan Yuan, Xin Wang, Robby T. Tan
In our DA framework, we retain the depth and background information during the domain feature alignment.
2 code implementations • CVPR 2022 • Fan Yan, Ming Nie, Xinyue Cai, Jianhua Han, Hang Xu, Zhen Yang, Chaoqiang Ye, Yanwei Fu, Michael Bi Mi, Li Zhang
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
1 code implementation • CVPR 2022 • Kehong Gong, Bingbing Li, Jianfeng Zhang, Tao Wang, Jing Huang, Michael Bi Mi, Jiashi Feng, Xinchao Wang
Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.
Ranked #15 on
3D Human Pose Estimation
on MPI-INF-3DHP
1 code implementation • CVPR 2022 • Yujing Xue, Jiageng Mao, Minzhe Niu, Hang Xu, Michael Bi Mi, Wei zhang, Xiaogang Wang, Xinchao Wang
We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.