Skeleton-aided Articulated Motion Generation
This addresses the problem of blurred motion in video generation for applications like animation or robotics, though it appears incremental as it builds on existing GAN methods.
The paper tackles generating articulated human motion sequences from a single image by using skeleton information and appearance reference with a conditional GAN and triplet loss, resulting in realistic motion sequences on KTH and Human3.6M datasets.
This work make the first attempt to generate articulated human motion sequence from a single image. On the one hand, we utilize paired inputs including human skeleton information as motion embedding and a single human image as appearance reference, to generate novel motion frames, based on the conditional GAN infrastructure. On the other hand, a triplet loss is employed to pursue appearance-smoothness between consecutive frames. As the proposed framework is capable of jointly exploiting the image appearance space and articulated/kinematic motion space, it generates realistic articulated motion sequence, in contrast to most previous video generation methods which yield blurred motion effects. We test our model on two human action datasets including KTH and Human3.6M, and the proposed framework generates very promising results on both datasets.