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            <h1 class="title is-1 publication-title">STMPL: Human Soft-Tissue Simulation</h1>
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                <a href="https://scholar.google.com/citations?hl=en&user=IsFcrLcAAAAJ&view_op=list_works&gmla=AILGF5WABKrCnUQBT0N6J6ehNV3hROKs-t_qseM-xUtMiD8w9sXD6K1hTGOCzu6aAyVe3WZnncoVRylOL7id9QRS" target="_blank">Anton Agafonov</a>,
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                  <a href="https://lihi.net.technion.ac.il/" target="_blank">Lihi Zelnik-Manor</a>
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                    <span class="author-block">Technion - Israel Institute of Technology</span>
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        STMPL able to simulate the soft tissue deformation of human bodies in real time is demonstrated through interactions with external interactions of varying shapes. We illustrate the capabilities of the STMPL model when simulating contact with grasping hands and 'ECCV' block letters, showcasing the detailed and realistic deformation such interactions produce. 
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            In various applications, such as virtual reality and gaming, simulating the deformation of soft tissues in the human body during interactions with external objects is essential. Traditionally, Finite Element Methods (FEM) have been employed for this purpose, but they tend to be slow and resource-intensive. In this paper, we propose a unified representation of human body shape and soft tissue with a data-driven simulator of non-rigid deformations. This approach enables rapid simulation of realistic interactions.

            Our method builds upon the SMPL model, which generates human body shapes considering rigid transformations. We extend SMPL by incorporating a soft tissue layer and an intuitive representation of external forces applied to the body during object interactions. Specifically, we mapped the 3D body shape and soft tissue and applied external forces to 2D UV maps. Leveraging a UNET architecture designed for 2D data, our approach achieves high-accuracy inference in real time. Our experiment shows that our method achieves plausible deformation of the soft tissue layer, even for unseen scenarios.
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            <strong>Method Overview.</strong> STMPL is based on a mapping between 3D (top) to 2D (bottom) of the body shape, the soft tissue layer, and the interacting external force. 
            The generation of training data is conducted utilizing a 3D FEM simulator. The training process (bottom) is executed in a 2D framework, where input parameters such as body shape, soft tissue thickness, and external force are provided, while the FEM-derived deformation, projected onto a 2D space, serves as the ground truth for the output.
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            <strong>Inference Pipeline.</strong>  Inference is performed in 2D and takes as input interactions with complex shapes with variability of soft tissue thickness. The generated 2D deformation is mapped back into 3D to obtain the final deformed mesh.
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            <strong>Soft Layer Generation.</strong> Top: five arms generated by the SMPL model with \( \beta_{2} = [-2,-1,0,1,2] \). Bottom: five arms with a range of thickness [0.5,0.625,0.75, 0.875, 1.0] cm.
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            <strong>Qualitative Comparison of FEM with Naive Baseline and STMPL (ours).</strong> 
            We visually compare simulated results of STMPL (right) of the hand grasp scenario (out-of-distribution example) with FEM ground truth (left) and Naive baseline model (middle).
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      <pre><code>
@misc{agafonov2024stmpl,
      title={STMPL: Human Soft-Tissue Simulation}, 
      author={Anton Agafonov and Lihi Zelnik-Manor},
      year={2024},
      eprint={2403.08344},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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