Poster: InstaPose: Dynamic Photography Feedback via Adaptive Pose Recommendation System

Many pose recommendation applications have been proposed and studied, primarily focusing on providing exercise feedback and fine-tuning postures to enhance precision for skill acquisition. These applications often utilize pose extraction and identification to help users perfect their form, whether in yoga, fitness routines, or other sports activities. However, these specialized applications are not generalizable for leisure use, such as capturing a photogenic or novel pose in front of an iconic tourist attraction. To address this issue, we present InstaPose, a mobile-friendly framework that delivers real-time pose recommendation and correction feedback. The core of our approach leverages Mobile Vision Transformers (MobileViT) to retrieve refined pose images from our dataset by matching the background context of the input photo. In addition, the proposed model outperforms VGG16 on our newly curated, large-scale high-quality dataset. This demonstrates the potential of our approach for real-time mobile pose guidance.