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..
Multi-Person 3D Motion Prediction with Multi-Range Transformers
Authors: Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform our experiments on multiple datasets including CMU-Mocap [1], Mu Po TS-3D [48], 3DPW [64] for multi-person motion prediction in 3D (with 2 3 persons). |
| Researcher Affiliation | Academia | Jiashun Wang1 Huazhe Xu2 Medhini Narasimhan2 Xiaolong Wang1 1UC San Diego 2UC Berkeley EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper describes the network architecture and its components in detail with mathematical equations, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page with code is available at https://jiashunwang.github.io/MRT/. |
| Open Datasets | Yes | We perform our experiments on multiple datasets. CMU-Mocap [1], Mu Po TS-3D [48] and 3DPW [64] datasets are collected using cameras with pose estimation and optimization. |
| Dataset Splits | No | The paper describes using CMU-Mocap as training data and sampling a test set, but it does not explicitly provide details about a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adam as an optimizer but does not specify any software libraries or frameworks with version numbers (e.g., PyTorch version, TensorFlow version, CUDA version). |
| Experiment Setup | Yes | In our experiments, we give 1 second history motion (k = 15 time steps) as input and recursively predict the future 3 seconds (45 time steps) as Sec. 3.3 described. We use L = 3 alternating layers with 8 heads in each Transformer. We use Adam [32] as the optimizer for our networks. During training, we set 3 10 4 as the learning rate for predictor P and 5 10 4 as the learning rate for discriminator D. We set λrec = 1 and λadv = 5 10 4. For experiments with 2 3 persons, we set a batch size of 32 and for scene with more people, we set a batch size of 8. |