# Generating Key Postures of Bharatanatyam Adavus with Pose Estimation

arXiv: https://arxiv.org/abs/2603.29570 · HTML: https://mlsanity.com/paper/2603.29570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
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- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [Rethinking FID: Towards a Better Evaluation Metric for Image Generation](https://mlsanity.com/paper/2401.09603/) (2023)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing](https://mlsanity.com/paper/1703.05446/) (2017)
- [Person Image Synthesis via Denoising Diffusion Model](https://mlsanity.com/paper/2211.12500/) (2022)
- [Disentangled Person Image Generation](https://mlsanity.com/paper/1712.02621/) (2017)
- [Synthesizing Images of Humans in Unseen Poses](https://mlsanity.com/paper/1804.07739/) (2018)
- [XingGAN for Person Image Generation](https://mlsanity.com/paper/2007.09278/) (2020)
- [InsetGAN for Full-Body Image Generation](https://mlsanity.com/paper/2203.07293/) (2022)
- [Improved Fine-Tuning by Better Leveraging Pre-Training Data](https://mlsanity.com/paper/2111.12292/) (2021)
- [The Conditional Analogy GAN: Swapping Fashion Articles on People Images](https://mlsanity.com/paper/1709.04695/) (2017)
- [Unsupervised Person Image Generation with Semantic Parsing Transformation](https://mlsanity.com/paper/1904.03379/) (2019)
- [POPDG: Popular 3D Dance Generation with PopDanceSet](https://mlsanity.com/paper/2405.03178/) (2024)
- [PCGAN: Partition-Controlled Human Image Generation](https://mlsanity.com/paper/1811.09928/) (2018)
- [Nrityantar: Pose oblivious Indian classical dance sequence classification system](https://mlsanity.com/paper/1812.05231/) (2018)
- [DGPose: Deep Generative Models for Human Body Analysis](https://mlsanity.com/paper/1804.06364/) (2018)

## Abstract (arXiv)

Preserving intangible cultural dances rooted in centuries of tradition and governed by strict structural and symbolic rules presents unique challenges in the digital era. Among these, Bharatanatyam, a classical Indian dance form, stands out for its emphasis on codified adavus and precise key postures. Accurately generating these postures is crucial not only for maintaining anatomical and stylistic integrity, but also for enabling effective documentation, analysis, and transmission to broader global audiences through digital means. We propose a pose-aware generative framework integrated with a pose estimation module, guided by keypoint-based loss and pose consistency constraints. These supervisory signals ensure anatomical accuracy and stylistic integrity in the synthesized outputs. We evaluate four configurations: standard conditional generative adversarial network (cGAN), cGAN with pose supervision, conditional diffusion, and conditional diffusion with pose supervision. Each model is conditioned on key posture class labels and optimized to maintain geometric structure. In both cGAN and conditional diffusion settings, the integrated pose guidance aligns generated poses with ground-truth keypoint structures, promoting cultural fidelity. Our results demonstrate that incorporating pose supervision significantly enhances the quality, realism, and authenticity of generated Bharatanatyam postures. This framework provides a scalable approach for the digital preservation, education, and dissemination of traditional dance forms, enabling high-fidelity generation without compromising cultural precision. Code is available at https://github.com/jagidsh/Generating-Key-Postures-of-Bharatanatyam-Adavus-with-Pose-Estimation.