PoseGen: Learning to Generate 3D Human Pose Dataset with NeRF
Authors: Mohsen Gholami, Rabab Ward, Z. Jane Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments show that the proposed Pose Gen improves two baseline models (SPIN and Hybr IK) on four datasets with an average 6% relative improvement. |
| Researcher Affiliation | Academia | Mohsen Gholami, Rabab Ward, Z. Jane Wang University of British Columbia, Vancouver, Canada {mgholami, rababw, zjanew}@ece.ubc.ca |
| Pseudocode | No | The paper describes the framework and components using textual descriptions and mathematical formulations, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Code is available at https: //github.com/mgholamikn/Pose Gen. |
| Open Datasets | Yes | We use three datasets that are not used during pretraining of pose estimator P, including 3DPW, AGORA, and SKI-Pose, for evaluation. To further evaluate the effectiveness of the proposed Pose Gen on boosting the performance of P on IND datasets used in the pre-training procedure, we also perform an evaluation on 3DHP. ... 3DPW (von Marcard et al. 2018) includes in-the-wild images... AGORA (Patel et al. 2021) uses 4240 high-quality textured scans of people... SKI-Pose (Sp orri 2016) is captured in a ski resort... MPI-INF-3DHP (3DHP) includes data from 8 subjects. |
| Dataset Splits | No | The paper mentions using training and test sets but does not provide specific details on validation splits (e.g., percentages or sample counts for validation data) for any of the datasets. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments (e.g., GPU models, CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using pre-trained models like Hybr IK and SPIN, and a Ne RF model (A-Ne RF), but does not specify software versions for these tools or for the underlying deep learning frameworks (e.g., PyTorch, TensorFlow) or other dependencies like Python or CUDA versions. |
| Experiment Setup | Yes | The overall loss of G is LG = w1Ladv + w2Lfb, (8)... In the above formula, d is a threshold to exclude samples with large errors." and "In Scenario 1, the objective of G is to generate plausible poses while minimizing the training loss of the pose estimator P: min G,P max D L(G, D, P). (1) In Scenario 2 the objective of G is to generate plausible poses while increasing the training loss of the pose estimator P: min G max D,P L(G, D, P). (2) |