1 code implementation • ECCV 2020 • Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, Bo Li
Deep neural networks (DNNs) have achieved great successes in various vision applications due to their strong expressive power.
1 code implementation • 16 Aug 2022 • Haonan Qiu, Yuming Jiang, Hang Zhou, Wayne Wu, Ziwei Liu
Notably, StyleFaceV is capable of generating realistic $1024\times1024$ face videos even without high-resolution training videos.
2 code implementations • 31 May 2022 • Yuming Jiang, Shuai Yang, Haonan Qiu, Wayne Wu, Chen Change Loy, Ziwei Liu
In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation.
no code implementations • 12 Apr 2022 • Haonan Qiu, Siyu Chen, Bei Gan, Kun Wang, Huafeng Shi, Jing Shao, Ziwei Liu
Realistic visual media synthesis is becoming a critical societal issue with the surge of face manipulation models; new forgery approaches emerge at an unprecedented pace.
no code implementations • ICCV 2021 • MingJie Sun, Zichao Li, Chaowei Xiao, Haonan Qiu, Bhavya Kailkhura, Mingyan Liu, Bo Li
Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm.
1 code implementation • 19 Jun 2019 • Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, Bo Li
In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples".
no code implementations • 28 Feb 2019 • Haonan Qiu, Chuan Wang, Hang Zhu, Xiangyu Zhu, Jinjin Gu, Xiaoguang Han
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
no code implementations • 13 Apr 2018 • Haonan Qiu, Yingbin Zheng, Hao Ye, Yao Lu, Feng Wang, Liang He
The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action.