22 papers with code • 0 benchmarks • 3 datasets
These leaderboards are used to track progress in Action Generation
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
Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability.
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.
Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language.
Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state.
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).
We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion.