Multi-Person 3D Motion Prediction with Multi-Range Transformers
Authors: Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 jiw077@ucsd.edu {huazhe_xu,medhini}@berkeley.edu xiw012@eng.ucsd.edu |
| 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. |