Learning 3D Garment Animation from Trajectories of A Piece of Cloth
Authors: YIDI SHAO, Chen Change Loy, Bo Dai
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that EUNet effectively delivers the energy gradients due to the deformations. |
| Researcher Affiliation | Collaboration | Yidi Shao1 Chen Change Loy1 Bo Dai2,3 1S-Lab, Nanyang Technological University 2The University of Hong Kong, 3Shanghai Artificial Intelligence Laboratory |
| Pseudocode | No | The paper describes methods using text and mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/ftbabi/EUNet_NeurIPS2024.git . |
| Open Datasets | Yes | To train our EUNet, we collect the dynamics of a piece of square cloth made of common materials, i.e., silk, leather, cotton, and denim. ... We evaluate the animations constrained by EUNet on ground truth garments from Cloth3D [2]. |
| Dataset Splits | Yes | We generate 800 sequences of different clothes for training and 200 sequences for test, with a length of 30 frames for each sequence. |
| Hardware Specification | Yes | We train our model for six epochs on V100. ... All experiments are run on NVIDIA A100-SXM4-80GB. |
| Software Dependencies | No | The paper mentions software like Blender and deep learning components (e.g., MLP, Adam optimizer) but does not provide specific version numbers for these software or any key libraries. |
| Experiment Setup | Yes | We apply four blocks of multi-layer perceptron (MLP) with dimensions 128. Each block of MLP consists of 2 fully connected layers... We set λ = 106 to ensure sufficient regularization. We adopt the Adam optimizer with an initial learning rate of 0.0002, with a decreasing factor of 0.5 every four epochs. The batch size is set to 4. We train our model for six epochs on V100. |