no code implementations • 17 Aug 2023 • Yuanzhi Wang, Yong Li, Xiaoya Zhang, Xin Liu, Anbo Dai, Antoni B. Chan, Zhen Cui
In addition to the utilization of a pretrained T2I 2D Unet for spatial content manipulation, we establish a dedicated temporal Unet architecture to faithfully capture the temporal coherence of the input video sequences.
1 code implementation • CVPR 2023 • Yong Li, Yuanzhi Wang, Zhen Cui
Specially, the representation of each modality is decoupled into two parts, i. e., modality-irrelevant/-exclusive spaces, in a self-regression manner.
1 code implementation • ICCV 2023 • Yuanzhi Wang, Zhen Cui, Yong Li
Recovering missed modality is popular in incomplete multimodal learning because it usually benefits downstream tasks.
no code implementations • 16 Sep 2021 • Yuanzhi Wang, Tao Lu, Yanduo Zhang, Junjun Jiang, JiaMing Wang, Zhongyuan Wang, Jiayi Ma
Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e. g., facial parsing maps, facial landmarks) to focus on facial structure, thereby maintaining the consistency of the facial structure while restoring facial details.
1 code implementation • 18 Apr 2021 • Yuanzhi Wang, Tao Lu, Yanduo Zhang, Yuntao Wu
However, many problems for scene reconversion and shadow estimation tasks, including uncalibrated feature information and poor semantic information, are still unresolved, thereby resulting in insufficient feature representation.
1 code implementation • 22 Oct 2020 • Tao Lu, Yuanzhi Wang, Yanduo Zhang, Yu Wang, Wei Liu, Zhongyuan Wang, Junjun Jiang
However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in reduced naturalness and fidelity of the reconstructed face, and further impairing the performance of downstream tasks (e. g., face detection, facial recognition).