no code implementations • 11 May 2023 • Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, Timo I. Denk
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures.
no code implementations • CVPR 2023 • Kun Su, Kaizhi Qian, Eli Shlizerman, Antonio Torralba, Chuang Gan
Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound.
no code implementations • ICCV 2023 • Mingfei Chen, Kun Su, Eli Shlizerman
The audio at the listener location is a complex outcome of sound propagation through the scene geometry and interacting with surfaces and also the locations of the emitters and the sounds they emit.
no code implementations • 3 Jun 2022 • Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey, Lawrence Staib, James S. Duncan
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.
no code implementations • NeurIPS 2021 • Kun Su, Xiulong Liu, Eli Shlizerman
It is often the case that the experience of watching the video can be enhanced by adding a musical soundtrack that is in-sync with the rhythmic features of these activities.
no code implementations • 7 Dec 2020 • Kun Su, Xiulong Liu, Eli Shlizerman
We propose a novel system that takes as an input body movements of a musician playing a musical instrument and generates music in an unsupervised setting.
1 code implementation • NeurIPS 2020 • Kun Su, Xiulong Liu, Eli Shlizerman
We present a novel system that gets as an input video frames of a musician playing the piano and generates the music for that video.
no code implementations • 12 Jun 2020 • Yang Zheng, Jinlin Xiang, Kun Su, Eli Shlizerman
The balanced learning strategy enables BI-MAML to both outperform other state-of-the-art models in terms of classification accuracy for existing tasks and also accomplish efficient adaption to similar new tasks with less required shots.
1 code implementation • CVPR 2020 • Kun Su, Xiulong Liu, Eli Shlizerman
Given inputs of body keypoints sequences obtained during various movements, our system associates the sequences with actions.
no code implementations • 29 May 2019 • Kun Su, Eli Shlizerman
We show that the methodology provides a high-quality unsupervised categorization of movements.