Differentiable Cloth Simulation for Inverse Problems
Authors: Junbang Liang, Ming Lin, Vladlen Koltun
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that our method can speed up backpropagation by two orders of magnitude. We demonstrate the presented approach on a number of inverse problems, including parameter estimation and motion control for cloth. ... We conduct three experiments to showcase the power of differentiable cloth simulation. |
| Researcher Affiliation | Academia | Junbang Liang Ming C. Lin University of Maryland, College Park Vladlen Koltun |
| Pseudocode | Yes | Algorithm 1 Cloth simulation |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | We used the real-world dataset from Wang et al. [30], which consists of 10 different cloth materials. |
| Dataset Splits | Yes | There are in total 50 frames of simulated data. The first 25 frames are taken as input and all 50 frames are used to measure accuracy. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for its experiments. |
| Software Dependencies | No | The paper mentions Py Torch [26] but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | In our optimization setup, we use SGD with learning rate ranging from 0.01 to 0.1 and momentum from 0.9 to 0.99, depending on the convergence speed. |