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Temporal Shape Transfer Network for 3D Human Motion

João Regateiro 1, 2 Edmond Boyer 1 
1 MORPHEO - Capture and Analysis of Shapes in Motion
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
2 MIMETIC - Analysis-Synthesis Approach for Virtual Human Simulation
UR2 - Université de Rennes 2, Inria Rennes – Bretagne Atlantique , IRISA-D5 - RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE
Abstract : This paper presents a learning-based approach to perform human shape transfer between an arbitrary 3D identity mesh and a temporal motion sequence of 3D meshes. Recent approaches tackle the human shape and pose transfer on a per-frame basis and do not yet consider the valuable information about the motion dynamics, e.g., body or clothing dynamics, inherently present in motion sequences. Recent datasets provide such sequences of 3D meshes, and this work investigates how to leverage the associated intrinsic temporal features in order to improve learning-based approaches on human shape transfer. These features are expected to help preserve temporal motion and identity consistency over motion sequences. To this aim, we introduce a new network architecture that takes as input successive 3D mesh frames in a motion sequence and which decoder is conditioned on the target shape identity. Training losses are designed to enforce temporal consistency between poses as well as shape preservation over the input frames. Experiments demonstrate substantially qualitative and quantitative improvements in using temporal features compared to optimization-based and recent learning-based methods.
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Contributor : Joao Regateiro Connect in order to contact the contributor
Submitted on : Friday, September 23, 2022 - 3:19:52 PM
Last modification on : Tuesday, October 25, 2022 - 4:25:10 PM


  • HAL Id : hal-03782133, version 1


João Regateiro, Edmond Boyer. Temporal Shape Transfer Network for 3D Human Motion. 3DV 2022 - International Conference on 3D Vision, Sep 2022, Prague / Hybrid, Czech Republic. pp.1-9. ⟨hal-03782133⟩



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