Learning Complex 3D Human Self-Contact
Authors: Mihai Fieraru, Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Vlad Olaru, Cristian Sminchisescu1343-1351
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To train models and for large-scale quantitative evaluation, we collect and annotate two large scale datasets containing images of people in self-contact. Human SC3D is an accurate 3d motion capture dataset containing 1, 032 sequences with 5, 058 contact events and 1, 246, 487 ground truth 3d poses synchronized with images captured from multiple views. We also collect Flickr SC3D, a dataset of 3, 969 images, containing 25, 297 annotations of body part region pairs in contact, defined on a 3d human surface model, together with their self-contact localisation in the image. The main contributions of the paper are as follows: |
| Researcher Affiliation | Academia | 1 Institute of Mathematics of the Romanian Academy, 2 Lund University |
| Pseudocode | No | The paper describes the model architecture and methodology in text and figures, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The data and models will be made available for research.1 http://vision.imar.ro/sc3d |
| Open Datasets | Yes | We collect two large datasets to support learning and evaluation: (1) Human SC3D, an accurate 3d motion capture repository containing 1, 032 sequences with 5, 058 contact events and 1, 246, 487 ground truth 3d poses synchronized with images collected from multiple views, and (2) Flickr SC3D, a repository of 3, 969 images, containing 25, 297 surface-to-surface correspondences with annotated image spatial support. ... The data and models will be made available for research.1 http://vision.imar.ro/sc3d |
| Dataset Splits | Yes | To assess the performance of our SCP network, we validate and test it on the Flickr SC3D dataset, which we split in the usual train (80%), test (10%) and validation (10%) subsets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | SCP takes as input an RGB image cropped around a person and learns to extract image space features Θfeat using the backbone of the Res Net50 (He et al. 2016) architecture, up to the 16th convolutional layer. The paper mentions |
| Experiment Setup | No | The paper states, |