# SMART: SMPLest-X Mesh Adaptation and RAFT Tracking for Soccer Pose Estimation Source: [SMART: SMPLest-X Mesh Adaptation and RAFT Tracking for Soccer Pose Estimation]() Collection index: [llms.txt]() Data updated: 2026-06-02T05:00:12.522Z Authors: Parthsarthi Rawat Published: 2026-05-29T17:12:34.000Z ## Abstract We present our approach to the FIFA Skeletal Tracking Challenge 2026, which requires estimating 3D world-space poses of soccer players from broadcast video. Our method finetunes SMPLest-X (ViT-H, 687 M parameters) via a stratified clip split, multi-task depth supervision, and broadcast augmentation, paired with a RAFT dense optical flow camera tracker, foot-plane anchoring, and two-pass temporal smoothing. Against the FIFA baseline score of 1.053 on the validation set, SMART achieves 0.647, a 38.6% improvement; on the held-out test set, SMART scores 0.593 (Global MPJPE: 0.324 m, Local MPJPE: 0.054 m). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Human Pose Estimation on FIFA Skeletal Tracking Challenge 2026 (val)](): 3D Human Pose Estimation; FIFA Skeletal Tracking Challenge 2026 (val); Global MPJPE: 0.37. - [3D Human Pose Estimation on FIFA Skeletal Tracking Challenge 2026 (test)](): 3D Human Pose Estimation; FIFA Skeletal Tracking Challenge 2026 (test); Global MPJPE: 0.324. ## Original sources and implementation [Original paper]() [PDF]()