UniPitch: Parametric Reconstruction of Soccer Matches from Broadcast Videos

Reconstructing soccer matches from broadcast videos requires identifying players across changing views and recovering their positions on the pitch. Existing pipelines estimate player tracks and camera geometry separately, then combine their outputs through projection. This separation leaves reconstructed player positions as downstream outputs, limiting their use as supervision for camera estimation. We present UniPitch, a framework for parametric reconstruction of soccer matches that represents persistent player identities, evolving pitch positions, and camera states within a common formulation. A differentiable geometric mapping allows two-dimensional player position errors to propagate back to camera parameters, enabling reconstruction supervision to guide the geometry itself. UniPitch learns persistent identity associations through appearance memory that outlives short-term tracks, models camera evolution using visual and temporal evidence, and refines player positions with identity-linked histories and geometric context. On SoccerNet-GSR, UniPitch achieves 73.30% P-HOTA, improving over the best decoupled baseline by 4.80%, with gains in both pitch-space matching and identity association. Ablation studies support the contributions of persistent identity memory, temporal camera modeling, and differentiable trajectory refinement.