# A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition

arXiv: https://arxiv.org/abs/1904.10681 · HTML: https://mlsanity.com/paper/1904.10681/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 16 of 16)

- [UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles](https://mlsanity.com/paper/2104.00946/) (2021)
- [A Survey on Human Interaction Motion Generation](https://mlsanity.com/paper/2503.12763/) (2025)
- [FLAG3D: A 3D Fitness Activity Dataset with Language Instruction](https://mlsanity.com/paper/2212.04638/) (2022)
- [PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data](https://mlsanity.com/paper/2212.04821/) (2022)
- [NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions](https://mlsanity.com/paper/2101.11529/) (2021)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [Diffusion Models in 3D Vision: A Survey](https://mlsanity.com/paper/2410.04738/) (2024)
- [Text-guided 3D Human Motion Generation with Keyframe-based Parallel Skip Transformer](https://mlsanity.com/paper/2405.15439/) (2024)
- [Hierarchical and Multimodal Data for Daily Activity Understanding](https://mlsanity.com/paper/2504.17696/) (2025)
- [Learning Joint Representation of Human Motion and Language](https://mlsanity.com/paper/2210.15187/) (2022)
- [Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows](https://mlsanity.com/paper/2404.17381/) (2024)
- [Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Karate Techniques](https://mlsanity.com/paper/2501.18729/) (2025)
- [SPG: Improving Motion Diffusion by Smooth Perturbation Guidance](https://mlsanity.com/paper/2503.02577/) (2025)
- [Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance](https://mlsanity.com/paper/2404.00054/) (2024)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)
- [From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control](https://mlsanity.com/paper/2506.00043/) (2025)

## Abstract (arXiv)

Current researches of action recognition mainly focus on single-view and multi-view recognition, which can hardly satisfies the requirements of human-robot interaction (HRI) applications to recognize actions from arbitrary views. The lack of datasets also sets up barriers. To provide data for arbitrary-view action recognition, we newly collect a large-scale RGB-D action dataset for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The dataset includes action samples captured in 8 fixed viewpoints and varying-view sequences which covers the entire 360 degree view angles. In total, 118 persons are invited to act 40 action categories, and 25,600 video samples are collected. Our dataset involves more participants, more viewpoints and a large number of samples. More importantly, it is the first dataset containing the entire 360 degree varying-view sequences. The dataset provides sufficient data for multi-view, cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance.