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- W3129098645 abstract "We aim to simultaneously estimate the 3D articulated pose and high fidelity volumetricoccupancy of human performance, from multiple viewpoint video (MVV) with asfew as two views. We use a multi-channel symmetric 3D convolutional encoder-decoderwith a dual loss to enforce the learning of a latent embedding that enables inference ofskeletal joint positions and a volumetric reconstruction of the performance. The inferenceis regularised via a prior learned over a dataset of view-ablated multi-view video footageof a wide range of subjects and actions, and show this to generalise well across unseensubjects and actions. We demonstrate improved reconstruction accuracy and lower poseestimation error relative to prior work on two MVV performance capture datasets: Human3.6M and TotalCapture." @default.
- W3129098645 created "2021-02-15" @default.
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- W3129098645 date "2020-08-05" @default.
- W3129098645 modified "2023-09-26" @default.
- W3129098645 title "Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras" @default.
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