Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Differentiable Cloth Simulation for Inverse Problems
Authors: Junbang Liang, Ming Lin, Vladlen Koltun
NeurIPS 2019 | Venue PDF | 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 ο¬rst 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. |