LCollision: Fast Generation of Collision-Free Human Poses using Learned Non-Penetration Constraints
Authors: Qingyang Tan, Zherong Pan, Dinesh Manocha3913-3921
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated our method on the SCAPE dataset (Anguelov et al. 2005), the MIT-Swing dataset (Vlasic et al. 2008), and the MIT Jumping dataset (Vlasic et al. 2008). Combining these techniques, we achieve an accuracy of 94.1%, a false positive rate of 6.1%, and a false negative rate of 5.7% when predicting collisions for 2.5 106 randomized human poses sampled from these datasets. Moreover, our learned collision detector is 80 faster than prior exact collision detection methods running on a CPU (Pan, Chitta, and Manocha 2012). In Table 1, we compare the accuracy of baselines in terms of predicting penetration depth energies, ranking penetration depth energies, and classifying collision-free meshes. |
| Researcher Affiliation | Academia | Qingyang Tan1, Zherong Pan2, Dinesh Manocha1 1 Department of Computer Science, University of Maryland at College Park 2 Department of Computer Science, University of Illinois at Urbana-Champaign qytan@umd.edu, zherong@illinois.edu, dmanocha@umd.edu |
| Pseudocode | Yes | Algorithm 1 Generating Penetration Energy Vector PDe |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We have evaluated our method on the SCAPE dataset (Anguelov et al. 2005), the MIT-Swing dataset (Vlasic et al. 2008), and the MIT Jumping dataset (Vlasic et al. 2008). |
| Dataset Splits | Yes | During the second stage, we use 0.7M samples of the augmented dataset for training and 0.3M samples for validation. |
| Hardware Specification | Yes | All the training and testing are performed on a single desktop machine with a 4-core CPU, 32GB memory, and an NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions 'We implement our method using Py Torch (Paszke et al. 2017)', but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | This stage uses the loss: L = w P DLP D + wrank Lrank + wentropy Lentropy, which is configured with w P D = 5, wrank = 2, wentropy = 2, and trained using a learning rate of 0.001 and a batch size of 32 over 30 epochs. We set Z0 = 10 for SCAPE and Z0 = 12 for Swing and Jumping. |