1 code implementation • ICCV 2023 • Fa-Ting Hong, Dan Xu
Talking head video generation aims to animate a human face in a still image with dynamic poses and expressions using motion information derived from a target-driving video, while maintaining the person's identity in the source image.
1 code implementation • 10 May 2023 • Fa-Ting Hong, Li Shen, Dan Xu
In this work, firstly, we present a novel self-supervised method for learning dense 3D facial geometry (ie, depth) from face videos, without requiring camera parameters and 3D geometry annotations in training.
1 code implementation • 22 Jun 2022 • Jia-Run Du, Jia-Chang Feng, Kun-Yu Lin, Fa-Ting Hong, Xiao-Ming Wu, Zhongang Qi, Ying Shan, Wei-Shi Zheng
Accordingly, we first exclude these surely non-existent categories by a complementary learning loss.
1 code implementation • CVPR 2022 • Fa-Ting Hong, Longhao Zhang, Li Shen, Dan Xu
In a more dense way, the depth is also utilized to learn 3D-aware cross-modal (i. e. appearance and depth) attention to guide the generation of motion fields for warping source image representations.
2 code implementations • 27 Jul 2021 • Fa-Ting Hong, Jia-Chang Feng, Dan Xu, Ying Shan, Wei-Shi Zheng
In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.
Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1
1 code implementation • Proceedings of the 29th ACM International Conference on Multimedia 2021 • Fa-Ting Hong, Jia-Chang Feng, Dan Xu, Ying Shan, Wei-Shi Zheng
In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.
Weakly-supervised Temporal Action Localization Weakly Supervised Temporal Action Localization
1 code implementation • CVPR 2021 • Jia-Chang Feng, Fa-Ting Hong, Wei-Shi Zheng
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations.
2 code implementations • 13 Aug 2020 • Ling-An Zeng, Fa-Ting Hong, Wei-Shi Zheng, Qi-Zhi Yu, Wei Zeng, Yao-Wei Wang, Jian-Huang Lai
However, most existing works focus only on video dynamic information (i. e., motion information) but ignore the specific postures that an athlete is performing in a video, which is important for action assessment in long videos.
Ranked #2 on Action Quality Assessment on Rhythmic Gymnastic
no code implementations • ECCV 2020 • Fa-Ting Hong, Xuanteng Huang, Wei-Hong Li, Wei-Shi Zheng
We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but without expensive supervision of manually annotating highlight segments.
1 code implementation • CVPR 2020 • Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng
Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern.
1 code implementation • CVPR 2019 • Wei-Hong Li, Fa-Ting Hong, Wei-Shi Zheng
In this work, we propose a deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling and feature learning.