Action Generation

14 papers with code • 0 benchmarks • 3 datasets

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Most implemented papers

Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction

clic-lab/ciff EMNLP 2018

We propose to decompose instruction execution to goal prediction and action generation.

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.

Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization

SHITIANYU-hue/Efficient-motion-planning 18 Apr 2019

Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability.

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.

Graph Constrained Reinforcement Learning for Natural Language Action Spaces

rajammanabrolu/KG-A2C ICLR 2020

Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language.

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.

Keep CALM and Explore: Language Models for Action Generation in Text-based Games

princeton-nlp/calm-textgame EMNLP 2020

In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state.

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.