Skinned Motion Retargeting with Dense Geometric Interaction Perception
Authors: Zijie Ye, Jia-Wei Liu, Jia Jia, Shikun Sun, Mike Zheng Shou
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the public Mixamo dataset and our newly-collected Scan Ret dataset demonstrate that Mesh Ret achieves state-of-the-art performance. Code available at https://github.com/abcyzj/Mesh Ret. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Technology, BNRist, Tsinghua University 2 Key Laboratory of Pervasive Computing, Ministry of Education 3 Show Lab, National University of Singapore |
| Pseudocode | Yes | Further details can be found in Algorithm 1. |
| Open Source Code | Yes | Code available at https://github.com/abcyzj/Mesh Ret. |
| Open Datasets | Yes | We trained and evaluated our method using the Mixamo dataset [2] and the newly curated Scan Ret dataset. ... We downloaded 3,675 motion clips performed by 13 cartoon characters from the Mixamo dataset contains, while the Scan Ret dataset consists of 8,298 clips executed by 100 human actors. ... Adobe. Mixamo. https://www.mixamo.com/. 2018. |
| Dataset Splits | No | The training set comprises 90% of the motion clips from both datasets, involving nine characters from Mixamo and 90 from Scan Ret. ... Details regarding the train/test split for specific motion sequences and characters are provided in the code. The paper specifies a 90% training split and discusses test splits, but it does not explicitly define a separate validation set with its size or percentage. |
| Hardware Specification | Yes | We implemented our network using Py Torch [23], running on a machine equipped with an NVIDIA RTX A6000 GPU and an AMD EPYC 9654 CPU. |
| Software Dependencies | No | We implemented our network using Py Torch [23]... The paper mentions PyTorch but does not provide a specific version number, nor does it list other software dependencies with their versions. |
| Experiment Setup | Yes | The hyper-parameters λrec, λdmi, λadv, λef, and L were empirically set to 1.0, 5.0, 1.0, 1.0, and 20, respectively. ... We employed the Adam optimizer [13] with a learning rate of 10 4 to optimize our network. The training process required 36 epochs. |