Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Ning, Jifeng"'
Audio-driven simultaneous gesture generation is vital for human-computer communication, AI games, and film production. While previous research has shown promise, there are still limitations. Methods based on VAEs are accompanied by issues of local ji
Externí odkaz:
http://arxiv.org/abs/2410.20359
Existing gesture generation methods primarily focus on upper body gestures based on audio features, neglecting speech content, emotion, and locomotion. These limitations result in stiff, mechanical gestures that fail to convey the true meaning of aud
Externí odkaz:
http://arxiv.org/abs/2410.09396
A comprehensive understanding of interested human-to-human interactions in video streams, such as queuing, handshaking, fighting and chasing, is of immense importance to the surveillance of public security in regions like campuses, squares and parks.
Externí odkaz:
http://arxiv.org/abs/2307.00464
This paper presents a novel approach for estimating human body shape and pose from monocular images that effectively addresses the challenges of occlusions and depth ambiguity. Our proposed method BoPR, the Body-aware Part Regressor, first extracts f
Externí odkaz:
http://arxiv.org/abs/2303.11675
Autor:
Liu, Jing, Hou, Jin, Liu, Dan, Zhao, Qijun, Chen, Rui, Chen, Xiaoyuan, Hull, Vanessa, Zhang, Jindong, Ning, Jifeng
Publikováno v:
In Ecological Informatics November 2024 83
Publikováno v:
In Computers and Electronics in Agriculture March 2024 218
Publikováno v:
In Pattern Recognition February 2024 146
Publikováno v:
In Biosystems Engineering January 2024 237:1-12
Publikováno v:
Signal Processing Volume 178, January 2021, 107805
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one. Noise contamination can often be caused during data acquisition and conversion. In this paper, we propose a novel spatial-spectral total variation (SSTV) r
Externí odkaz:
http://arxiv.org/abs/2006.00235
Higher-order low-rank tensor arises in many data processing applications and has attracted great interests. Inspired by low-rank approximation theory, researchers have proposed a series of effective tensor completion methods. However, most of these m
Externí odkaz:
http://arxiv.org/abs/2005.14521