Generating Key Postures of Bharatanatyam Adavus with Pose Estimation

Abstract

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.

Figures (7)

• Figure 1: Visual comparison of posture alignment across classical dance forms. a: Bharatanatyam postures illustrating vertical, horizontal, and diagonal axes and body symmetry (source GeometryInMotion2021). b: Kathak poses emphasizing upright torso and mirrored hand gestures (source Mallick2017KathakGeometry). c: Ballet positions such as arabesque and plié, showing turnout and axial balance (source Daprati2009DanceToTheMusicBalletBasics2015). Despite cultural differences, these styles share a common reliance on geometrically structured key postures, justifying alignment-preserving approaches in generative modeling.
• Figure 2: Illustration of Kuditta Tattal Adavu sequence in Bharatanatyam showcasing the structured progression of body postures. Each frame represents a temporally ordered key pose, reflecting the codified nature of movement transitions in the dance form.
• Figure 3: Overview of the proposed model. A conditional generative model generates a dance image $I_{\text{gen}}$ from a latent code $z$ and a key posture label. Pose Estimation Module is used to extract 2D skeletal keypoints. Keypoint Loss -- ($\mathcal{L}_{\text{kp}}$) average squared Euclidean distance between normalized corresponding keypoints from $I_{\text{gen}}$ and $I_{\text{real}}$, enforcing pixel-wise accuracy of each joint. Pose Consistency Loss -- ($\mathcal{L}_{\text{pose}}$) difference in internal pose structure capturing relative distances and angles between joints—between the generated and real poses.
• Figure 4: Comparison of generated Adavu key postures across four models—(a) Conditional GAN (cGAN), (b) cGAN with Pose Estimation (CGPE), (c) Conditional Diffusion, and (d) Conditional Diffusion with Pose Estimation (CDMPE). Real reference images (ground truth) are shown alongside for comparison. While all models produce visually plausible results, the diffusion-based models demonstrate smoother textures and higher fidelity. The Conditional Diffusion with Pose Estimation (d) exhibits the most anatomically accurate and culturally faithful postures, preserving structural constraints and symbolic hand-gesture alignment essential to Bharatanatyam.
• Figure 5: Complete set of generated Adavu key postures ($\mathbf{128 \times 128}$) using the Conditional Diffusion with Pose Estimation model. All 20 representative postures are synthesized to reflect structural precision, anatomical symmetry, and cultural authenticity specific to Bharatanatyam. This visualization highlights the model’s ability to generalize across a diverse set of codified movements while preserving stylistic consistency.