Human action generation

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

Libraries

Datasets

Subtasks

Most implemented papers

Conditional Generative Adversarial Nets

Content

Human Action Generation with Generative Adversarial Networks

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Structure-Aware Human-Action Generation

Action2Motion: Conditioned Generation of 3D Human Motions

Action-Conditioned 3D Human Motion Synthesis with Transformer VAE

Generative Adversarial Graph Convolutional Networks for Human Action Synthesis

MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion