Chirality Nets for Human Pose Regression
Authors: Raymond Yeh, Yuan-Ting Hu, Alexander Schwing
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate chirality nets on the task of human pose regression... We demonstrate the generalization and effectiveness of our approach on three pose regression tasks over four datasets... Our approach achieves/matches state-of-the-art results... |
| Researcher Affiliation | Academia | Raymond A. Yeh , Yuan-Ting Hu*, Alexander G. Schwing Department of Electrical Engineering, University of Illinois at Urbana-Champaign {yeh17, ythu2, aschwing}@illinois.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The Pytorch implementation and unit-tests of the proposed layers are part of the supplementary material. We have also included a short Jupyter notebook demo to illustrate the key concepts. |
| Open Datasets | Yes | We evaluate on two standard datasets, the Human3.6M [22] and the Human Eva I [49]. |
| Dataset Splits | Yes | We use the same train and test subject splits. |
| Hardware Specification | No | The paper states, 'We thank NVIDIA for providing GPUs used for this work and Cisco for access to the Arcetri cluster,' but it does not specify any particular GPU models, CPU models, memory details, or detailed cluster specifications. |
| Software Dependencies | No | The paper mentions 'Pytorch implementation' and uses optimizers like 'Adam' and 'SGD,' and tools like 'Open Pose,' but it does not provide specific version numbers for these software components or libraries. |
| Experiment Setup | Yes | Our model follows the supervised training procedure and network design of Pavllo et al. [42]. Our network is the identical temporal convolutional network architecture, where each layer is replaced with its chiral version, i.e., 1D dilated convolution, batch-normalization, and dropout layers. We also replace ReLU non-linearities with Tanh to achieve equivariance. |