Synthetic-to-Real Pose Estimation with Geometric Reconstruction
Authors: Qiuxia Lin, Kerui Gu, Linlin Yang, Angela Yao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments4.1 Datasets & Evaluation Metric4.2 Implementation Details4.3 Comparison with the State-of-the-Art4.4 AnalysisTable 1: Hand 2D keypoint detection. Our method has the best average PCK@0.05 on both datasets and the most significant improvements on MCP and Fin. |
| Researcher Affiliation | Academia | 1Department of Computer Science, National University of Singapore 2State Key Laboratory of Media Convergence and Communication, CUC 3School of Information and Communication Engineering, CUC {qiuxia, keruigu, ayao}@comp.nus.edu.sg |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | No concrete access to source code for the methodology described in this paper is provided. |
| Open Datasets | Yes | For the source domain, we consider the synthetic dataset RHD [43] with 44k rendered images for training. For the unlabeled target domain, we consider real-world datasets H3D [41] and MVHand [39]... SURREAL [31] is a synthetic human dataset... Human3.6M [8] annotates 3.6 million real-world indoor human poses. 3DPW [32] is a challenging outdoor dataset... |
| Dataset Splits | Yes | For the unlabeled target domain, we consider real-world datasets H3D [41] and MVHand [39]; these have 11k/2k and 42k/42k training/testing splits respectively... Human3.6M [8]... We follow protocol 1, using S1 and S5-S8 for training and S9 and S11 for testing. 3DPW [32] is a challenging outdoor dataset with 24 videos for training and 24 videos for testing. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions ResNet-101 and Adam optimizer but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The initial learning rate is 1e-4, and we used Adam optimizer and degraded the learning rate with 0.1 at steps 60 and 75. Pre-training is done with a batch size of 64 for 40 epochs; fine-tuning is done with a batch size of 32 for 80 epochs. The hyperparameters λa, λr, λp are set as 0.1. |