Pranjal Kumar
Papers
1
Total Citations
4
H-Index
1
About
Pranjal Kumar is a researcher in computer vision and human pose estimation, with a focus on improving the robustness and accuracy of regression-based models. His most-cited work, "Towards improvement of baseline performance for regression based human pose estimation" (2023), addresses critical limitations in standard pose estimation pipelines, proposing enhancements that boost baseline performance without sacrificing efficiency. This contribution has garnered early recognition with 4 citations, signaling its relevance to ongoing efforts in the field. Kumar’s research bridges the gap between theoretical model design and practical deployment, targeting applications in human-computer interaction, sports analytics, and augmented reality. By refining key components such as loss functions and architectural choices, his work provides a strong foundation for future advances in 2D and 3D pose estimation. As an emerging voice in computer vision, Kumar’s methodical approach to benchmarking and performance improvement offers valuable insights for students and researchers seeking to build more reliable and scalable pose estimation systems.
Research Focus
Key Achievements
Top Papers
- 1