Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Conference on Information and Knowledge Management (CIKM)
Dense subgraph detection is a fundamental building block for a variety of applications. Most of the existing methods aim to discover dense subgraphs within either a single network or a multi-view network while ignoring the informative node dependencies across multiple layers of networks in a complex system. To date, it largely remains a daunting task to detect dense subgraphs on multi-layered networks. In this paper, we formulate the problem of dense subgraph detection on multi-layered networks based on cross-layer consistency principle. We further propose a novel algorithm DESTINE based on projected gradient descent with the following advantages. First, armed with the cross-layer dependencies, DESTINE is able to detect significantly more accurate and meaningful dense subgraphs at each layer. Second, it scales linearly w.r.t. the number of links in the multi-layered network. Extensive experiments demonstrate the efficacy of the proposed DESTINE algorithm in various cases.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Ilkan Esiyok, Pascal Berrang, Katriel Cohn-Gordon, Robert Künnemann