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SMART method boosts soccer player pose estimation by 38.6%

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called SMART for estimating the 3D poses of soccer players from broadcast video. This approach fine-tunes the SMPLest-X model and incorporates RAFT dense optical flow tracking, along with other enhancements like foot-plane anchoring and temporal smoothing. SMART significantly improved upon the FIFA baseline score, achieving a 38.6% enhancement on the validation set and a score of 0.593 on the test set. AI

IMPACT This method sets a new benchmark for 3D pose estimation in sports, potentially improving sports analytics and broadcast technologies.

RANK_REASON The cluster contains an academic paper detailing a new method for pose estimation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Parthsarthi Rawat ·

    SMART: SMPLest-X Mesh Adaptation and RAFT Tracking for Soccer Pose Estimation

    arXiv:2605.31551v1 Announce Type: new 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 c…

  2. arXiv cs.CV TIER_1 English(EN) · Parthsarthi Rawat ·

    SMART: SMPLest-X Mesh Adaptation and RAFT Tracking for Soccer Pose Estimation

    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 bro…

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