Spatio-Temporal Human Shape Completion with Implicit Function Networks


Boyao ZHOU, Jean-Sebastien Franco, Federica Bogo and Edmond Boyer


We address the problem of inferring a human shape from partial observations, such as depth images, in temporal sequences. Deep Neural Networks (DNN) have been shown successful to estimate detailed shapes on a frame-by-frame basis but consider yet little or no temporal information over frame sequences for detailed shape estimation. Recently, networks that implicitly encode shape occupancy using MLP layers have shown very promising results for such single-frame shape inference, with the advantage of reducing the dimensionality of the problem and providing continuously encoded results. In this work we propose to generalize implicit encoding to spatio-temporal shape inference with spatio-temporal implicit function networks or STIF-Nets, where temporal redundancy and continuity is expected to improve the shape and motion quality. To validate these added benefits, we collect and train with motion data from CAPE for dressed humans, and DFAUST for body shapes with no clothing. We show our model's ability to estimate shapes for a set of input frames, and interpolate between them. Our results show that our method outperforms existing state of the art methods, both in accuracy and computational efficiency, in particular outperforming single-frame methods for detailed shape estimation.

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  Important Dates

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Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021