NAP: Neural 3D Articulated Object Prior
Authors: Jiahui Lei, Congyue Deng, William B Shen, Leonidas J. Guibas, Kostas Daniilidis
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate our high performance in articulated object generation as well as its applications on conditioned generation, including Part2Motion, Part Net-Imagination, Motion2Part, and GAPart2Object. We examine 4 important questions with our experiments: (1) How can we evaluate articulated object generation? (Sec. 4.1) (2) How well does NAP capture the distribution of articulated objects? (Sec. 4.2) (3) How effective is each of NAP s components? (Sec. 4.3) (4) What applications can NAP enable? (Sec. 4.4) |
| Researcher Affiliation | Academia | Jiahui Lei1 Congyue Deng2 Bokui Shen2 Leonidas Guibas2 Kostas Daniilidis13 1 University of Pennsylvania 2 Stanford University 3 Archimedes, Athena RC |
| Pseudocode | No | The paper describes its method in Section 3 and illustrates it with figures, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a project URL (https://www.cis.upenn.edu/~leijh/projects/nap) on the first page, but there is no explicit statement in the paper about releasing the source code or a direct link to a code repository. |
| Open Datasets | Yes | We train all the methods on Part Net-Mobility [39] across all categories jointly, with a maximum 8 rigid parts (K = 8) and a train-val-test split ratio [0.7, 0.1, 0.2]. [39] Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, et al. Sapien: A simulated part-based interactive environment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11097 11107, 2020. |
| Dataset Splits | Yes | We train all the methods on Part Net-Mobility [39] across all categories jointly, with a maximum 8 rigid parts (K = 8) and a train-val-test split ratio [0.7, 0.1, 0.2]. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper refers to various models and datasets but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks like PyTorch or TensorFlow versions). |
| Experiment Setup | No | The paper describes the dataset, splits, and network architecture, but does not provide specific experimental setup details such as learning rates, batch sizes, number of epochs, or optimizer settings. |