ProtoRes: Proto-Residual Architecture for Deep Modeling of Human Pose

• URL: http://arxiv.org/abs/2106.01981v1
• Date: Thu, 3 Jun 2021 16:56:58 GMT
• Title: ProtoRes: Proto-Residual Architecture for Deep Modeling of Human Pose
• Authors: Boris N. Oreshkin and Florent Bocquelet and F\'elix H. Harvey and Bay Raitt and Dominic Laflamme
• Abstract summary: We tackle the problem of constructing a full static human pose based on sparse and variable user inputs. We propose a novel neural architecture that combines residual connections with prototype encoding of a partially specified pose to create a new complete pose. We develop a user interface to integrate our neural model in Unity, a real-time 3D development platform.
• Score: 6.9997407868865364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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