no code implementations • 29 May 2024 • Hanchao Liu, Xiaohang Zhan, Shaoli Huang, Tai-Jiang Mu, Ying Shan
This problem is characterized by an open and fully customizable set of motion control tasks.
no code implementations • 14 Mar 2024 • Duotun Wang, Hengyu Meng, Zeyu Cai, Zhijing Shao, Qianxi Liu, Lin Wang, Mingming Fan, Ying Shan, Xiaohang Zhan, Zeyu Wang
We present HeadEvolver, a novel framework to generate stylized head avatars from text guidance.
no code implementations • 28 Nov 2023 • Xian Liu, Xiaohang Zhan, Jiaxiang Tang, Ying Shan, Gang Zeng, Dahua Lin, Xihui Liu, Ziwei Liu
In this paper, we propose an efficient yet effective framework, HumanGaussian, that generates high-quality 3D humans with fine-grained geometry and realistic appearance.
no code implementations • 31 Oct 2023 • Xin He, Shaoli Huang, Xiaohang Zhan, Chao Weng, Ying Shan
Our framework comprises a Semantic Enhancement module and a Context-Attuned Motion Denoiser (CAMD).
no code implementations • 19 Oct 2023 • Jiaxu Zhang, Shaoli Huang, Zhigang Tu, Xin Chen, Xiaohang Zhan, Gang Yu, Ying Shan
In this work, we present TapMo, a Text-driven Animation Pipeline for synthesizing Motion in a broad spectrum of skeleton-free 3D characters.
no code implementations • CVPR 2023 • Zhongjin Luo, Shengcai Cai, Jinguo Dong, Ruibo Ming, Liangdong Qiu, Xiaohang Zhan, Xiaoguang Han
However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming.
no code implementations • 17 Aug 2022 • Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu
A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes).
3 code implementations • 15 Jun 2022 • Jiahao Xie, Wei Li, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy
We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models.
no code implementations • CVPR 2022 • Tianxin Tao, Xiaohang Zhan, Zhongquan Chen, Michiel Van de Panne
Motion style transfer is a common method for enriching character animation.
1 code implementation • NeurIPS 2021 • Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy
Extensive experiments on COCO show that ORL significantly improves the performance of self-supervised learning on scene images, even surpassing supervised ImageNet pre-training on several downstream tasks.
2 code implementations • ICCV 2021 • Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, Ping Luo
Unlike most recent methods that focused on improving accuracy of image classification, we present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches to learn discriminative representations for object detection.
no code implementations • 26 Nov 2020 • Xiangxiang Chu, Xiaohang Zhan, Bo Zhang
Recent unsupervised contrastive representation learning follows a Single Instance Multi-view (SIM) paradigm where positive pairs are usually constructed with intra-image data augmentation.
2 code implementations • 26 Aug 2020 • Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy
In this work, we present a comprehensive empirical study to better understand the role of inter-image invariance learning from three main constituting components: pseudo-label maintenance, sampling strategy, and decision boundary design.
1 code implementation • CVPR 2020 • Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Yew Soon Ong, Chen Change Loy
In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly.
2 code implementations • CVPR 2020 • Xiaohang Zhan, Xingang Pan, Bo Dai, Ziwei Liu, Dahua Lin, Chen Change Loy
This is achieved via Partial Completion Network (PCNet)-mask (M) and -content (C), that learn to recover fractions of object masks and contents, respectively, in a self-supervised manner.
3 code implementations • CVPR 2020 • Lei Yang, Dapeng Chen, Xiaohang Zhan, Rui Zhao, Chen Change Loy, Dahua Lin
With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters.
1 code implementation • ECCV 2020 • Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, Ping Luo
Learning a good image prior is a long-term goal for image restoration and manipulation.
no code implementations • CVPR 2020 • Ziwei Liu, Zhongqi Miao, Xingang Pan, Xiaohang Zhan, Dahua Lin, Stella X. Yu, Boqing Gong
A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e. g., sunny weather) for achieving high performance on the test data in a target domain (e. g., rainy weather).
1 code implementation • ICCV 2019 • Xingang Pan, Xiaohang Zhan, Jianping Shi, Xiaoou Tang, Ping Luo
Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods.
Ranked #6 on Robust Object Detection on DWD
2 code implementations • CVPR 2019 • Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu
We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.
4 code implementations • CVPR 2019 • Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin
Face recognition sees remarkable progress in recent years, and its performance has reached a very high level.
1 code implementation • CVPR 2019 • Xiaohang Zhan, Xingang Pan, Ziwei Liu, Dahua Lin, Chen Change Loy
Instead of explicitly modeling the motion probabilities, we design the pretext task as a conditional motion propagation problem.
6 code implementations • ECCV 2018 • Xiaohang Zhan, Ziwei Liu, Junjie Yan, Dahua Lin, Chen Change Loy
Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected.
no code implementations • 2 Dec 2017 • Xiaohang Zhan, Ziwei Liu, Ping Luo, Xiaoou Tang, Chen Change Loy
The key of this new form of learning is to design a proxy task (e. g. image colorization), from which a discriminative loss can be formulated on unlabeled data.