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The task of reconstructing 3D human motion has wideranging applications. Thegold standard Motion capture (MoCap) systems are accurate but inaccessible tothe general public due to their cost, hardware and space constraints. Incontrast, monocular human mesh recovery (HMR) methods are much more accessiblethan MoCap as they take single-view videos as inputs. Replacing the multi-viewMo- Cap systems with a monocular HMR method would break the current barriers tocollecting accurate 3D motion thus making exciting applications like motionanalysis and motiondriven animation accessible to the general public. However,performance of existing HMR methods degrade when the video contains challengingand dynamic motion that is not in existing MoCap datasets used for training.This reduces its appeal as dynamic motion is frequently the target in 3D motionrecovery in the aforementioned applications. Our study aims to bridge the gapbetween monocular HMR and multi-view MoCap systems by leveraging informationshared across multiple video instances of the same action. We introduce theNeural Motion (NeMo) field. It is optimized to represent the underlying 3Dmotions across a set of videos of the same action. Empirically, we show thatNeMo can recover 3D motion in sports using videos from the Penn Action dataset,where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.To further validate NeMo using 3D metrics, we collected a small MoCap datasetmimicking actions in Penn Action,and show that NeMo achieves better 3Dreconstruction compared to various baselines.

NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action