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..
MotionMixer: MLP-based 3D Human Body Pose Forecasting
Authors: Arij Bouazizi, Adrian Holzbock, Ulrich Kressel, Klaus Dietmayer, Vasileios Belagiannis
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation protocols. For all evaluations, we demonstrate state-of-the-art performance, while having a model with a smaller number of parameters. |
| Researcher Affiliation | Collaboration | 1Mercedes-Benz AG, Stuttgart, Germany 2Ulm University, Ulm, Germany 3Otto von Guericke University Magdeburg, Magdeburg, Germany |
| Pseudocode | No | The paper includes figures and mathematical formulations, but does not contain explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/MotionMLP/MotionMixer. |
| Open Datasets | Yes | Human3.6M. [Ionescu et al., 2013]... AMASS. [Mahmood et al., 2019]... 3DPW. The 3D Pose in the Wild dataset [von Marcard et al., 2018] |
| Dataset Splits | Yes | Human3.6M. Following [Sofianos et al., 2021; Mao et al., 2020], we use the subject (S11) for validation, (S5) for testing, and the rest of the subjects for training. AMASS. Following [Sofianos et al., 2021; Mao et al., 2021], we select 8 datasets for training, 4 for validation, and one (BMLrub) as the test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions using 'Adam' as an optimizer but does not specify version numbers for any key software components or libraries. |
| Experiment Setup | Yes | In each MLP block, a dropout layer with a rate of 0.1 is added to prevent overfitting. We use Adam [Kingma and Ba, 2014] as the optimizer. During training, the learning rate is set to 10-2 and decayed by a factor of 0.1 every 10 epochs. We train our model for 50 epochs with a batch size of 50 for Human3.6M and 256 for AMASS. |