Automated video looping with progressive dynamism

By Zicheng Liao, Neel Joshi, Hugues Hoppe. 2013

53 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. In-So Kweon
4. Kristen Grauman
5. Dacheng Tao
6. Jitendra Malik
7. Xiaogang Wang
8. Trevor Darrell

In Control and Systems Engineering

1. Frédo Durand
2. William T. Freeman
3. Wojciech Matusik
4. Baoyuan Wang

Citations in the corpus

• Learning-based Video Motion Magnification (2018, methodology): “For the cinemagraph application [12], we add a dynamism control function by a post-synthesis approach using our method.”
• Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks (2016, neutral): “Thus, in our work, rather than modeling a motion prior as a context-free distribution, we propose to model the conditional motion distribution of future frames given an input image by incorporating a series of low-and high-level image cues.”
• Personalized Cinemagraphs Using Semantic Understanding and Collaborative Learning (2017, background): “[6], the context feature information is not used; hence, any link between the user and context information may not be reflected to u.”