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Estimating the 3D position of human joints has become a widely researchedtopic in the last years. Special emphasis has gone into defining novel methodsthat extrapolate 2-dimensional data (keypoints) into 3D, namely predicting theroot-relative coordinates of joints associated to human skeletons. The latestresearch trends have proven that the Transformer Encoder blocks aggregatetemporal information significantly better than previous approaches. Thus, wepropose the usage of these models to obtain more accurate 3D predictions byleveraging temporal information using attention mechanisms on ordered sequenceshuman poses in videos.  Our method consistently outperforms the previous best results from theliterature when using both 2D keypoint predictors by 0.3 mm (44.8 MPJPE, 0.7%improvement) and ground truth inputs by 2mm (MPJPE: 31.9, 8.4% improvement) onHuman3.6M. It also achieves state-of-the-art performance on the HumanEva-Idataset with 10.5 P-MPJPE (22.2% reduction). The number of parameters in ourmodel is easily tunable and is smaller (9.5M) than current methodologies(16.95M and 11.25M) whilst still having better performance. Thus, our 3Dlifting model's accuracy exceeds that of other end-to-end or SMPL approachesand is comparable to many multi-view methods.

LiftFormer: 3D Human Pose Estimation using attention models