BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
Authors: Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu8553-8560
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we describe experimental details of Bi HMPGAN along with analysis of both qualitative and quantitative results on two publicly available datasets; viz. a) Human 3.6M (Ionescu et al. 2014) and CMU MOCAP. The full pipeline of Bi HMP-GAN is implemented in tensorflow with ADAM optimizer. We use a batch size of 32 |
| Researcher Affiliation | Academia | Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu Video Analytics Lab, Department of Computational and Data Sciences Indian Institute of Science, Bangalore, India. jogendrak@iisc.ac.in, maharshigor18@gmail.com, venky@iisc.ac.in |
| Pseudocode | Yes | Algorithm 1: Training algorithm for Bi HMP-GAN, with explicit enforcement of direct content loss. |
| Open Source Code | No | The paper mentions using 'publicly available implementation' for HP-GAN, but does not provide concrete access to its own source code for Bi HMP-GAN. |
| Open Datasets | Yes | In this section we describe experimental details of Bi HMPGAN along with analysis of both qualitative and quantitative results on two publicly available datasets; viz. a) Human 3.6M (Ionescu et al. 2014) and CMU MOCAP. |
| Dataset Splits | No | The paper mentions following data selection criteria from a previous work but does not explicitly state the training, validation, and test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'implemented in tensorflow' and 'ADAM optimizer' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use a batch size of 32 with learning rate set at 0.00005. Single layer LSTM (Chung et al. 2014) with 512 hidden units is incorporated as a recurrent architecture for both sequence encoder, decoder and bidirectional discriminator network. Following previous motion prediction works (Li et al. 2018; Martinez, Black, and Romero 2017) the length of intrinsic past pose sequence is set to 50, i.e. 2 seconds of skeleton motion at 25 fps setting. Considering fair evaluation on long-term prediction, the length of predicted motion sequence is set to 25. |