# Quo Vadis, Skeleton Action Recognition ?

arXiv: https://arxiv.org/abs/2007.02072 · HTML: https://mlsanity.com/paper/2007.02072/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition](https://mlsanity.com/paper/2107.08580/) (2021)
- [Holistic Interaction Transformer Network for Action Detection](https://mlsanity.com/paper/2210.12686/) (2022)
- [Holistic Representation Learning for Multitask Trajectory Anomaly Detection](https://mlsanity.com/paper/2311.01851/) (2023)
- [Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition](https://mlsanity.com/paper/2110.14994/) (2021)
- [Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition](https://mlsanity.com/paper/2210.01459/) (2022)
- [NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions](https://mlsanity.com/paper/2101.11529/) (2021)
- [SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2309.05834/) (2023)
- [ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild](https://mlsanity.com/paper/2205.05177/) (2022)
- [The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition](https://mlsanity.com/paper/2105.13061/) (2021)
- [Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack](https://mlsanity.com/paper/2211.11312/) (2022)
- [Action Selection Learning for Multi-label Multi-view Action Recognition](https://mlsanity.com/paper/2410.03302/) (2024)
- [Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition](https://mlsanity.com/paper/2207.09767/) (2022)
- [Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour](https://mlsanity.com/paper/2208.03819/) (2022)
- [MultiSensor-Home: A Wide-area Multi-modal Multi-view Dataset for Action Recognition and Transformer-based Sensor Fusion](https://mlsanity.com/paper/2504.02287/) (2025)
- [Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion](https://mlsanity.com/paper/2305.19480/) (2023)
- [Using joint angles based on the international biomechanical standards for human action recognition and related tasks](https://mlsanity.com/paper/2406.17443/) (2024)
- [GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes](https://mlsanity.com/paper/2307.14713/) (2023)
- [I2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation](https://mlsanity.com/paper/2310.15568/) (2023)
- [Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation](https://mlsanity.com/paper/2409.18297/) (2024)
- [Dark Transformer: A Video Transformer for Action Recognition in the Dark](https://mlsanity.com/paper/2407.12805/) (2024)
- [ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models](https://mlsanity.com/paper/2610.16134/) (2026)

## Abstract (arXiv)

In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To study skeleton-action recognition in the wild, we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. We extend our study to include out-of-context actions by introducing Skeleton-Mimetics, a dataset derived from the recently introduced Mimetics dataset. We also introduce Metaphorics, a dataset with caption-style annotated YouTube videos of the popular social game Dumb Charades and interpretative dance performances. We benchmark state-of-the-art models on the NTU-120 dataset and provide multi-layered assessment of the results. The results from benchmarking the top performers of NTU-120 on the newly introduced datasets reveal the challenges and domain gap induced by actions in the wild. Overall, our work characterizes the strengths and limitations of existing approaches and datasets. Via the introduced datasets, our work enables new frontiers for human action recognition.