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..
Reconstruction of Manipulated Garment with Guided Deformation Prior
Authors: Ren Li, Corentin Dumery, Zhantao Deng, Pascal Fua
NeurIPS 2024 | Venue PDF | 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 EMAIL EMAIL EMAIL EMAIL |
| 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. |