# Learning a Deep Model for Human Action Recognition from Novel Viewpoints

arXiv: https://arxiv.org/abs/1602.00828 · HTML: https://mlsanity.com/paper/1602.00828/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2016.

## Built on by (most cited first, 12 of 12)

- [Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving](https://mlsanity.com/paper/2205.14882/) (2022)
- [Analysis and Evaluation of Kinect-based Action Recognition Algorithms](https://mlsanity.com/paper/2112.08626/) (2021)
- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2304.00858/) (2023)
- [Unsupervised View-Invariant Human Posture Representation](https://mlsanity.com/paper/2109.08730/) (2021)
- [An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video](https://mlsanity.com/paper/2404.06741/) (2024)
- [ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2107.14285/) (2021)
- [Cross-view Action Recognition via Contrastive View-invariant Representation](https://mlsanity.com/paper/2305.01733/) (2023)
- [Beyond the Horizon: Decoupling Multi-View UAV Action Recognition via Partial Order Transfer](https://mlsanity.com/paper/2504.20530/) (2025)
- [Detecting Violence in Video Based on Deep Features Fusion Technique](https://mlsanity.com/paper/2204.07443/) (2022)
- [A Critical Analysis on Machine Learning Techniques for Video-based Human Activity Recognition of Surveillance Systems: A Review](https://mlsanity.com/paper/2409.00731/) (2024)
- [FedCVU: Federated Learning for Cross-View Video Understanding](https://mlsanity.com/paper/2603.21647/) (2026)

## Abstract (arXiv)

Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a non-linear virtual path that connects the views. The R-NKTM is learned from dense trajectories of synthetic 3D human models fitted to real motion capture data and generalizes to real videos of human actions. The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to incorporate new action classes. R-NKTM is learned with dummy labels and does not require knowledge of the camera viewpoint at any stage. Experiments on three benchmark cross-view human action datasets show that our method outperforms existing state-of-the-art.