https://github.com/v-sekai/tool_cloth_dynamics
https://github.com/v-sekai/tool_cloth_dynamics
Last synced: 22 days ago
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- Host: GitHub
- URL: https://github.com/v-sekai/tool_cloth_dynamics
- Owner: V-Sekai
- License: mit
- Created: 2022-10-17T03:49:01.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-08T17:03:30.000Z (23 days ago)
- Last Synced: 2025-04-09T20:12:50.745Z (22 days ago)
- Language: C++
- Homepage:
- Size: 255 MB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DiffCloth
Customized by V-Sekai
Code repository for our paper [DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact](https://people.csail.mit.edu/liyifei/publication/diffcloth-differentiable-cloth-simulator/)
[📃 Paper](https://people.csail.mit.edu/liyifei/uploads/diffcloth-highres-tog.pdf) | [🌍 Project](https://people.csail.mit.edu/liyifei/publication/diffcloth/)
### Tested Operating Systems
Ubuntu 22.04 | Mac OS 15 | Windows 11
### 1. Optimize/Visualize Section 6 Experiments:
- Run optimization:
```
just build
./DiffCloth -demo -mode optimize -seed {randseed}
```
where `{demooptions}` is the name of the demos from the following options and `{randseed}` is an integer for random initialization of the initial guesses
of the tasks.
The corresponding option for each of the experiments is:
- T-shirt: `tshirt`
- Sphere: `sphere`
- Hat: `hat`
- Sock: `sock`
- Dress: `dress`
The progress of the optimization is saved into the `output/` directory of the root folder.
### Blender Addon For Selecting vertex indexes
```python
import bpy
# Ensure that we are in object mode
bpy.ops.object.mode_set(mode='OBJECT')
# Get the active object (assumes it's a mesh)
obj = bpy.context.active_object
# Get the selected vertices using list comprehension
selected_vertices = [v.index for v in obj.data.vertices if v.select]
# Print the list of selected vertices
print(selected_vertices)
```
### Citation
Please consider citing our paper if your find our research or this codebase helpful:
@article{Li2022diffcloth,
author = {Li, Yifei and Du, Tao and Wu, Kui and Xu, Jie and Matusik, Wojciech},
title = {DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact},
year = {2022},
issue_date = {February 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {42},
number = {1},
issn = {0730-0301},
url = {https://doi.org/10.1145/3527660},
doi = {10.1145/3527660},
abstract = {Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications. Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact [Ly et al. 2020]. We draw inspiration from previous work [Du et al. 2021] to propose a fast and novel method for deriving gradients in PD-based cloth simulation with dry frictional contact. Furthermore, we conduct a comprehensive analysis and evaluation of the usefulness of gradients in contact-rich cloth simulation. Finally, we demonstrate the efficacy of our simulator in a number of downstream applications, including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design, and real-to-sim transfer. We observe a substantial speedup obtained from using our gradient information in solving most of these applications.},
journal = {ACM Trans. Graph.},
month = {oct},
articleno = {2},
numpages = {20},
keywords = {differentiable simulation, cloth simulation, Projective Dynamics}
}