no code implementations • 26 Apr 2024 • Zhengze Xu, Mengting Chen, Zhao Wang, Linyu Xing, Zhonghua Zhai, Nong Sang, Jinsong Lan, Shuai Xiao, Changxin Gao
To generate coherent motions, we first leverage the Kalman filter to construct smooth crops in the focus tunnel and inject the position embedding of the tunnel into attention layers to improve the continuity of the generated videos.
no code implementations • 23 Apr 2024 • Haozhe Cheng, Cheng Ju, Haicheng Wang, Jinxiang Liu, Mengting Chen, Qiang Hu, Xiaoyun Zhang, Yanfeng Wang
The denoised text classes help OVAR models classify visual samples more accurately; in return, classified visual samples help better denoising.
no code implementations • 19 Mar 2024 • Mengting Chen, Xi Chen, Zhonghua Zhai, Chen Ju, Xuewen Hong, Jinsong Lan, Shuai Xiao
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way.
no code implementations • 5 Dec 2023 • Xi Chen, Zhiheng Liu, Mengting Chen, Yutong Feng, Yu Liu, Yujun Shen, Hengshuang Zhao
In particular, considering the facts that (1) text can only describe motions roughly (e. g., regardless of the moving speed) and (2) text may include both content and motion descriptions, we introduce a motion intensity estimation module as well as a text re-weighting module to reduce the ambiguity of text-to-motion mapping.
no code implementations • 13 Jan 2021 • Mengting Chen, Xinggang Wang, Heng Luo, Yifeng Geng, Wenyu Liu
By applying the proposed feature matching block in different layers of the few-shot recognition network, multi-scale information among the compared images can be incorporated into the final cascaded matching feature, which boosts the recognition performance further and generalizes better by learning on relationships.
1 code implementation • 31 Dec 2019 • Mengting Chen, Yuxin Fang, Xinggang Wang, Heng Luo, Yifeng Geng, Xin-Yu Zhang, Chang Huang, Wenyu Liu, Bo wang
The learning problem of the sample generation (i. e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works.