Action Generation
24 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Action Generation
HumanAct12
PHSPD
Two4Two
Most implemented papers
CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective
In this paper, we propose a novel cognitive framework of individual interaction.
COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities
indexfziq/comma
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COLING 2022
Motivations, emotions, and actions are inter-related essential factors in human activities.
Text Editing as Imitation Game
shininglab/text-editing-as-imitation-game
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Text editing, such as grammatical error correction, arises naturally from imperfect textual data.
Learning Physically Realizable Skills for Online Packing of General 3D Shapes
We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems.
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.
Language-free Compositional Action Generation via Decoupling Refinement
xliu443/language-free-compositional-action-generation-via-decoupling-refinement
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Composing simple elements into complex concepts is crucial yet challenging, especially for 3D action generation.
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time.
ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning
lamda-rl/act
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Third, we train an Advantage-Conditioned Transformer (ACT) to generate actions conditioned on the estimated advantages.
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
Large Language Models (LLMs) have shown promise in the autonomous driving sector, particularly in generalization and interpretability.
Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
oliviazwy/dcgcn
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With potential applications in fields including intelligent surveillance and human-robot interaction, the human motion prediction task has become a hot research topic and also has achieved high success, especially using the recent Graph Convolutional Network (GCN).
