no code implementations • 9 Mar 2023 • Yucheng Xu, Li Nanbo, Arushi Goel, Zijian Guo, Zonghai Yao, Hamidreza Kasaei, Mohammadreze Kasaei, Zhibin Li
Videos depict the change of complex dynamical systems over time in the form of discrete image sequences.
no code implementations • 9 Mar 2022 • Cian Eastwood, Li Nanbo, Christopher K. I. Williams
Given two object images, how can we explain their differences in terms of the underlying object properties?
1 code implementation • NeurIPS 2020 • Li Nanbo, Cian Eastwood, Robert B. Fisher
In order to sidestep the main technical difficulty of the multi-object-multi-view scenario -- maintaining object correspondences across views -- MulMON iteratively updates the latent object representations for a scene over multiple views.
no code implementations • NeurIPS 2021 • Li Nanbo, Muhammad Ahmed Raza, Hu Wenbin, Zhaole Sun, Robert B. Fisher
We train DyMON on multi-view-dynamic-scene data and show that DyMON learns -- without supervision -- to factorize the entangled effects of observer motions and scene object dynamics from a sequence of observations, and constructs scene object spatial representations suitable for rendering at arbitrary times (querying across time) and from arbitrary viewpoints (querying across space).
no code implementations • 1 Jan 2021 • Li Nanbo, Robert Burns Fisher
Learning object-centric scene representations is crucial for scene structural understanding.