# NAPA: Neural Art Human Pose Amplifier

arXiv: https://arxiv.org/abs/2012.08501 · HTML: https://mlsanity.com/paper/2012.08501/

## Key facts

- Cited by 2 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification](https://mlsanity.com/paper/2408.01827/) (2024)

## Abstract (arXiv)

This is the project report for CSCI-GA.2271-001. We target human pose estimation in artistic images. For this goal, we design an end-to-end system that uses neural style transfer for pose regression. We collect a 277-style set for arbitrary style transfer and build an artistic 281-image test set. We directly run pose regression on the test set and show promising results. For pose regression, we propose a 2d-induced bone map from which pose is lifted. To help such a lifting, we additionally annotate the pseudo 3d labels of the full in-the-wild MPII dataset. Further, we append another style transfer as self supervision to improve 2d. We perform extensive ablation studies to analyze the introduced features. We also compare end-to-end with per-style training and allude to the tradeoff between style transfer and pose regression. Lastly, we generalize our model to the real-world human dataset and show its potentiality as a generic pose model. We explain the theoretical foundation in Appendix. We release code at https://github.com/strawberryfg/NAPA-NST-HPE, data, and video.