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arXiv Computer Vision By Marc Peral, Guillem Capellera, Luis Ferraz, Antonio Rubio, Stella Gra{\ss}hof, Dan Witzner Hansen, Antonio Agudo

Unified Trajectory and 3D Pose Forecasting for Multi-Agent Sports Analysis

Read the original on arXiv Computer Vision →

The paper introduces a unified model for forecasting both global trajectories and 3D body poses of multiple agents in sports settings. It uses a socio‑temporal encoder that fuses trajectory and pose inputs, shared factorized attention, per‑joint loss reweighting, and a frequency‑domain smoothing term to improve predictions. The authors also correct evaluation biases by adopting a non‑overlapping protocol and demonstrate state‑of‑the‑art results on WorldPose and 3DPW datasets, especially in pose accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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