Human action generation
10 papers with code • 7 benchmarks • 8 datasets
Yan et al. (2019) CSGN:
"When the dancer is stepping, jumping and spinning on the stage, attentions of all audiences are attracted by the streamof the fluent and graceful movements. Building a model that is capable of dancing is as fascinating a task as appreciating the performance itself. In this paper, we aim to generate long-duration human actions represented as skeleton sequences, e.g. those that cover the entirety of a dance, with hundreds of moves and countless possible combinations."
( Image credit: Convolutional Sequence Generation for Skeleton-Based Action Synthesis )
Benchmarks
These leaderboards are used to track progress in Human action generation
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Human3.6M
NTU RGB+D
NTU RGB+D 120
HumanAct12
NTU RGB+D 2D
UESTC RGB-D
PHSPD
FLAG3D
Latest papers with no code
Convolutional Sequence Generation for Skeleton-Based Action Synthesis
It captures the temporal structure at multiple scales through the GP prior and the temporal convolutions; and establishes the spatial connection between the latent vectors and the skeleton graphs via a novel graph refining scheme.
Deep Video Generation, Prediction and Completion of Human Action Sequences
In the second stage, a skeleton-to-image network is trained, which is used to generate a human action video given the complete human pose sequence generated in the first stage.
