2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
[3B3-E-2-01]Design a Loss Function which Generates a Spatial configuration of Image In-betweening
〇Paulino Cristovao1, Hidemoto Nakada1,2, Yusuke Tanimura1,2, Hideki Asoh2(1. University of Tsukuba, 2. National Advanced Institute of Science and Technology of Japan (AIST))
Instead of generating image inbetween directly from adjacent frames, we propose a method based on inbetweening in latent space. We design a simple loss function which generates a latent space that represent the spatial configu- ration of image inbetween. Contrary to the frame based methods, this model can make plausible assumption about the moving objects in the image and can capture what is not seen in the images. Our model has three networks, all based on variational autoencoder, sharing same weights. We validate this model on different synthetic datasets. We show the details of our network architecture and the evaluation results.