Human action generation

8 papers with code • 4 benchmarks • 7 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 )

Most implemented papers

Conditional Generative Adversarial Nets

eriklindernoren/PyTorch-GAN 6 Nov 2014

Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models.

Human Action Generation with Generative Adversarial Networks

xingchenzhao/Generating-Human-Skeletons-with-Mutual-Actions-WGAN-Pytorch 26 May 2018

Inspired by the recent advances in generative models, we introduce a human action generation model in order to generate a consecutive sequence of human motions to formulate novel actions.

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

zheshiyige/Learning-Diverse-Stochastic-Human-Action-Generators-by-Learning-Smooth-Latent-Transitions AAAI 2019

In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality.

Structure-Aware Human-Action Generation

PingYu-iris/SA-GCN ECCV 2020

Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence.

Action2Motion: Conditioned Generation of 3D Human Motions

EricGuo5513/action-to-motion 30 Jul 2020

Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.

Action-Conditioned 3D Human Motion Synthesis with Transformer VAE

Mathux/ACTOR ICCV 2021

By sampling from this latent space and querying a certain duration through a series of positional encodings, we synthesize variable-length motion sequences conditioned on a categorical action.

Generative Adversarial Graph Convolutional Networks for Human Action Synthesis

degardinbruno/kinetic-gan 21 Oct 2021

Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning).

MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion

skelemoa/mugl 21 Oct 2021

We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion.