We develop a novel joint-view Kalman filter for causal reconstruction of compressed-sensed multiview videos. Compressed-sensed multiview video frames are initially reconstructed individually via ℓ1-norm minimization. Then, ajoint-view state transition model is established for each pair of neighboring views using motion or motion-disparity field estimates. Experimental results demonstrate significantly improved reconstruction quality compared to conventional CS reconstruction and independent-view (single-view) motion-compensated Kalman filtering.