19,850 research outputs found
Skeleton-aided Articulated Motion Generation
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.Comment: ACM MM 201
Signature inversion for monotone paths
The aim of this article is to provide a simple sampling procedure to
reconstruct any monotone path from its signature. For every N, we sample a
lattice path of N steps with weights given by the coefficient of the
corresponding word in the signature. We show that these weights on lattice
paths satisfy the large deviations principle. In particular, this implies that
the probability of picking up a "wrong" path is exponentially small in N. The
argument relies on a probabilistic interpretation of the signature for monotone
paths
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