{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/napa-neural-art-human-pose-amplifier","title":"NAPA: Neural Art Human Pose Amplifier","arxiv_id":"2012.08501","date":"2020-12-15","proceeding":null,"authors":["Qingfu Wan","Oliver Lu"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2012.08501v1","url_pdf":"https://arxiv.org/pdf/2012.08501v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"napa-neural-art-human-pose-amplifier","repo_url":"https://github.com/strawberryfg/NAPA-NST-HPE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}