Neural Shape Deformation Priors
Authors: Jiapeng Tang, Lev Markhasin, Bi Wang, Justus Thies, Matthias Niessner
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach in experiments using the Deforming Thing4D dataset, and compare to both classic optimization-based and recent neural network-based methods.Our experiments and ablation studies demonstrate that our method can be applied to challenging new deformations. |
| Researcher Affiliation | Collaboration | Jiapeng Tang1 Lev Markhasin2 Bi Wang2 Justus Thies3 Matthias Nießner1 1 Technical University of Munich 2 Sony Europe RDC Stuttgart 3 Max Planck Institute for Intelligent Systems, Tübingen, Germany |
| Pseudocode | No | The paper does not contain structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See the supplemental material. |
| Open Datasets | Yes | Our experiments are performed on the Deforming Thing4D-Animals [39] dataset which contains 1494 non-rigidly deforming animations...In addition, we also include comparisons on another animal dataset used in TOSCA [71]. |
| Dataset Splits | No | For the train/test split, we divide all animations into training (1296) and test (198). The paper specifies train and test data splits but does not explicitly provide details for a separate validation split. |
| Hardware Specification | No | The paper mentions running experiments but does not provide specific details on the hardware used, such as GPU/CPU models or cluster specifications, within the main text. |
| Software Dependencies | No | The paper mentions using "PyTorch" and "Adam" but does not specify their exact version numbers required for reproducibility. |
| Experiment Setup | Yes | We use an Adam [73] optimizer with β1 = 0.9, β2 = 0.999, and ϵ = 10 8. In the first stage, we train the forward and backward deformation networks individually. Specifically, the backward and forward deformation networks are respectively optimized by the objective described in Equations 6 or 7 using a batch size of 16 with the learning rate of 5e-4 for 100 epochs. In the second stage, the whole model is trained according to Equation 8 in an end-to-end manner using a batch size of 6 with a learning rate of 5e-5 for 20 epochs. |