Motion Denoising
Motion Denoising is a computational method in motion capture that refines noisy motion data by suppressing artifacts and preserving essential kinematic features through signal processing or machine learning techniques.
93
Publications
4.5K
Citations
328
Authors
167
Institutions
Publications per year
2017–2026
49
Parents
93
PhysDiff: Physics-Guided Human Motion Diffusion Model
Ye Yuan, Jiaming Song, Umar Iqbal et al. · 2023 · 191 citations
Computing, Computer Science, Physics +15
Denoising image sequences does not require motion estimation
Antoni Buades, B. Coll, Jean‐Michel Morel · 2006 · 148 citations
Blur-Invariant Deep Learning for Blind-Deblurring
Akash Singh, A. N. Rajagopalan · 2017 · 143 citations
1–5 of 93
328
Hong Kong University of Science and Technology, Hong Kong SAR China
3 publications · 81 citations
3
h-index
Institut de Recherche en Informatique et Systèmes Aléatoires, France
3 publications · 328 citations
3
h-index
3
h-index
2
h-index
2
h-index
1–5 of 328
167
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
3 h-index
7 publications
674 citations

Ghent University
Ghent, Belgium
3 h-index
10 publications
394 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
12 publications
324 citations
Centre Inria de l'Université de Rennes
Rennes, France
3 h-index
7 publications
674 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
2 h-index
5 publications
399 citations
Northwestern University
Evanston, United States
2 h-index
4 publications
134 citations
Tsinghua University
Beijing, China
2 h-index
6 publications
345 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
5 publications
1.8K citations
1–8 of 167
4 publications · 154 citations
4
h-index
Repository · 4 publications · 95 citations
4
h-index
3 publications · 70 citations
3
h-index
3 publications · 182 citations
3
h-index
3 publications · 121 citations
3
h-index
1–5