Estimating　the　full　3D　hand　shape　and　pose　from　a　single　RGB　image　is　essential　for　virtual　reality　and　human-computer　interaction　applications,　but　it　remains　a　challenging　task.　While　current　hand　reconstruction　methods　have　achieved　high　accuracy,　they　often　come　with　heavy　computational　costs,　limiting　their　ability　to　perform　real-time　inference.　To　tackle　this　issue,　we　present　a　novel　learning-based　network　architecture　that　consists　of　two　key　components:　(1)　a　3D　hand　joint　position　detection　module　that　efficiently　extracts　joint　positions　from　hand　images,　and　(2)　an　inverse　kinematics　module　that　precisely　predicts　joint　rotations　in　a　single　feed　forward　pass.　Comprehensive　evaluations　across　several　datasets　demonstrate　that　our　method　generates　accurate　and　realistic　3D　hand　meshes　while　outperforming　existing　methods　in　3D　hand　pose　estimation.　©　2024　IEEE.