Reconstruction of Manipulated Garment with Guided Deformation Prior
Authors: Ren Li, Corentin Dumery, Zhantao Deng, Pascal Fua
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superior reconstruction accuracy of our method compared to previous ones, especially when dealing with large non-rigid deformations arising from the manipulations.We validate our approach on the data from the VR-Folding dataset [2], where point clouds are generated from multi-view RGBD images. |
| Researcher Affiliation | Academia | Ren Li Corentin Dumery Zhantao Deng Pascal Fua Computer Vision Lab, EPFL Lausanne, Switzerland ren.li@epfl.ch corentin.dumery@epfl.ch zhantao.deng@epfl.ch pascal.fua@epfl.ch |
| Pseudocode | No | No section or figure explicitly labeled 'Pseudocode' or 'Algorithm' is present. |
| Open Source Code | Yes | Our implementation and model weights are available at https://github.com/liren2515/GarmentFolding. |
| Open Datasets | Yes | We train our models using data from the VR-Folding [2] and CLOTH3D [55] datasets. |
| Dataset Splits | No | The paper explicitly mentions 'training and test splits' but does not specify a separate 'validation' split. |
| Hardware Specification | Yes | All the models are trained using the Adam optimizer [63] on NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions software components and architectures like U-Net and Adam optimizer but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We train IΘ and AΦ jointly for 9000 iterations with a batch size of 50.The diffusion model is trained for 100 epochs, with a learning rate of 1e-4, a batch size of 64, and T = 1000 steps.We choose K = 128 and train G for 100 epochs, using a learning rate of 1e-4 and a batch size of 128. |