Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders

31 August 2020

Papers citing "Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders"

9 / 9 papers shown

Title
Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences Yi-Ling Qiao Lin Gao Yu-Kun Lai Shihong Xia AI4CE 36 12 0 04 Oct 2018
Variational Autoencoders for Deforming 3D Mesh Models Qingyang Tan Lin Gao Yu-kun Lai Shi-hong Xia AI4CE 70 200 0 13 Sep 2017
Sparse Data Driven Mesh Deformation Lin Gao Yu-kun Lai Jie Yang Ling-Xiao Zhang Leif Kobbelt Shi-hong Xia 55 74 0 05 Sep 2017
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence Or Litany Tal Remez Emanuele Rodolà A. Bronstein M. Bronstein 3DPC 149 286 0 27 Apr 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 412 1,823 0 25 Nov 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 810 3,287 0 24 Nov 2016
Unsupervised Learning of Video Representations using LSTMs Nitish Srivastava Elman Mansimov Ruslan Salakhutdinov SSL 135 2,591 0 16 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.9K 150,115 0 22 Dec 2014
Generating Sequences With Recurrent Neural Networks Alex Graves GAN 155 4,039 0 04 Aug 2013