Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
Authors: Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu8553-8560
AAAI 2019 | Venue PDF | 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. EMAIL, EMAIL, EMAIL |
| 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. |