Multiview Human Body Reconstruction from Uncalibrated Cameras
Authors: Zhixuan Yu, Linguang Zhang, Yuanlu Xu, Chengcheng Tang, LUAN TRAN, Cem Keskin, Hyun Soo Park
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our calibration-free multiview fusion approach on multiple datasets varying from indoor to outdoor, controlled to in-the-wild environments. We refer the reader to check additional results, experiments and implementation details in the supplementary material. |
| Researcher Affiliation | Collaboration | Zhixuan Yu University of Minnesota yu000064@umn.edu Linguang Zhang Meta Reality Labs linguang@meta.com Yuanlu Xu Meta Reality Labs Research yuanluxu@meta.com Chengcheng Tang Meta Reality Labs chengcheng.tang@meta.com Luan Tran Meta Reality Labs tranluan07@meta.com Cem Keskin Meta Reality Labs cemkeskin@meta.com Hyun Soo Park University of Minnesota hspark@umn.edu |
| Pseudocode | No | The paper describes the method using figures and equations but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Human3.6M1 [15] is a large-scale multiview dataset with ground truth 3D human pose annotation... UP-3D [26] is an in-the-wild single view dataset... MARCOn I [6] is a multiview dataset... VBR [2] is a multiview dataset... |
| Dataset Splits | No | We follow the standard training/testing split: using subject S1, S5, S6, S7 and S8 for training, and subject S9 and S11 for testing. We use the standard training split [24, 52]. The paper does not explicitly mention a 'validation' split or provide specific percentages/counts for the splits beyond subject IDs. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper mentions using a 'Res Net-50 backbone' and a 'pre-trained Dense Pose model' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We design the encoder f E( ; θE) as a Res Net-50 backbone [12], that takes a 224 224 3 image as an input and outputs a global feature vector with 256 dimensions and a 56 56 local feature map with 256 dimensions... We set τ = 0.05 and σ = 2.33 10 2. |