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
Most implemented papers
Conditional Generative Adversarial Nets
MaximeVandegar/Papers-in-100-Lines-of-Code
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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
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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.
Action-Conditioned 3D Human Motion Synthesis with Transformer VAE
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.
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
zheshiyige/Learning-Diverse-Stochastic-Human-Action-Generators-by-Learning-Smooth-Latent-Transitions
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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
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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
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Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
Generative Adversarial Graph Convolutional Networks for Human Action Synthesis
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
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We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion.
Action-conditioned On-demand Motion Generation
We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization.
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
2024-MindSpore-1/Code9
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CVPR 2023
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision.
