DSPP: Deep Shape and Pose Priors of Humans

Hu, Shanfeng, Shum, Hubert and Mucherino, Antonio (2019) DSPP: Deep Shape and Pose Priors of Humans. In: MIG 2019 : 12th annual ACM/SIGGRAPH conference on Motion, Interaction and Games, 28-30 Oct 2019, Newcastle upon Tyne, England. (In Press)

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Abstract

The prior knowledge of real human body shapes and poses is fundamentalin computer games and animation (e.g. performance capture). Linear subspaces such as the popular SMPL model have a limited capacity to represent the large geometric variations of human shapes and poses. What is worse is that random sampling from them often produces non-realistic humans because the distribution of real humans is more likely to concentrate on a non-linear manifold instead of the full subspace. Towards this problem, we propose to learn human shape and pose manifolds using a more powerful deep generator network, which is trained to produce samples that cannot be distinguished from real humans by a deep discriminator network. In contrast to previous work that learn both the generator and discriminator in the original geometry spaces, we learn them in the more representative latent spaces discovered by a shape and a pose auto-encoder network respectively. Random sampling from our priors produces higher-quality human shapes and poses. The capacity of our priors is best applied to applications such as virtual human synthesis in games.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: human shape modelling, human pose modelling, generative adversarial networks, deep learning
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 16 Sep 2019 13:23
Last Modified: 11 Oct 2019 13:05
URI: http://nrl.northumbria.ac.uk/id/eprint/40699

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