Human motion transfer on 3D avatars has witnessed substantial progress, driven by the advancements of 3D pose estimation usingRGB data. This technology analyzes human movements captured through RGB cameras, enabling tracking of 3D body landmarks andleading to the animation of 3D avatars. Utilizing RGB input offers a range of advantages, democratizing avatar creation by eliminatingthe need for specialized equipment, such as sensors, markers, or specialized studios. Recent years have seen remarkable strides in thisfield, leveraging deep learning models and sophisticated computer vision algorithms to capture intricate movements and gesturesfrom RGB video footage. This study introduces a novel real-time approach leveraging RGB input to generate realistic 3D animations. Itcomprises three phases: i) 3D human pose estimation using MediaPipe, ii) correction of MediaPipe’s landmarks’ inaccuracies, especiallyregarding depth dimension, and incorporation of bones’ rotation information, and, finally, iii) transfer of the motion to the target 3Davatar. © Ilias Poulios | ACM 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Digital Library, https://doi.org/10.1145/3672406.3672427.