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- W3172331374 abstract "Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling. Specifically, we tackle the problem of constructing a full static human pose based on sparse and variable user inputs (e.g. locations and/or orientations of a subset of body joints). To solve this problem, we propose a novel neural architecture that combines residual connections with prototype encoding of a partially specified pose to create a new complete pose from the learned latent space. We show that our architecture outperforms a baseline based on Transformer, both in terms of accuracy and computational efficiency. Additionally, we develop a user interface to integrate our neural model in Unity, a real-time 3D development platform. Furthermore, we introduce two new datasets representing the static human pose modeling problem, based on high-quality human motion capture data, which will be released publicly along with model code." @default.
- W3172331374 created "2021-06-22" @default.
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- W3172331374 date "2021-06-03" @default.
- W3172331374 modified "2023-09-27" @default.
- W3172331374 title "ProtoRes: Proto-Residual Architecture for Deep Modeling of Human Pose." @default.
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- W3172331374 hasPublicationYear "2021" @default.
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