# SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans

arXiv: https://arxiv.org/abs/2004.00326 · HTML: https://mlsanity.com/paper/2004.00326/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 17 of 17)

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- [Temporal Residual Jacobians For Rig-free Motion Transfer](https://mlsanity.com/paper/2407.14958/) (2024)
- [A structured latent space for human body motion generation](https://mlsanity.com/paper/2106.04387/) (2021)
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- [Implicit Neural Representation for Physics-driven Actuated Soft Bodies](https://mlsanity.com/paper/2401.14861/) (2024)
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- [Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks](https://mlsanity.com/paper/2201.10122/) (2022)
- [DSNet: Dynamic Skin Deformation Prediction by Recurrent Neural Network](https://mlsanity.com/paper/2201.07660/) (2021)
- [AnthroNet: Conditional Generation of Humans via Anthropometrics](https://mlsanity.com/paper/2309.03812/) (2023)
- [STMPL: Human Soft-Tissue Simulation](https://mlsanity.com/paper/2403.08344/) (2024)
- [SOMA: From Surface Observations to Muscle Anatomy](https://mlsanity.com/paper/2606.09246/) (2026)

## Abstract (arXiv)

We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion. Datasets to learn such task are scarce and expensive to generate, which makes training models prone to overfitting. At the core of our method there are three key contributions that enable us to model highly realistic dynamics and better generalization capabilities than state-of-the-art methods, while training on the same data. First, a novel motion descriptor that disentangles the standard pose representation by removing subject-specific features; second, a neural-network-based recurrent regressor that generalizes to unseen shapes and motions; and third, a highly efficient nonlinear deformation subspace capable of representing soft-tissue deformations of arbitrary shapes. We demonstrate qualitative and quantitative improvements over existing methods and, additionally, we show the robustness of our method on a variety of motion capture databases.