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..
Representing Spatial Trajectories as Distributions
Authors: Didac Suris Coll-Vinent, Carl Vondrick
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show our method s advantage over baselines in prediction tasks. Our experiments on human movement datasets show that our method can accurately predict the past and future of a trajectory segment, as well as the interpolation between two different segments, outperforming autoregressive baselines. Additionally, it can do so for any continuous point in time. |
| Researcher Affiliation | Academia | Dídac Surís Columbia University EMAIL Carl Vondrick Columbia University EMAIL |
| Pseudocode | No | The paper describes the model architecture and training process but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | See trajectories.cs.columbia.edu for video results and code. Yes, they are in the supplementary materials. |
| Open Datasets | Yes | We extract human movement trajectories from the Fine Gym [45], Diving48 [29] and Fis V [57] datasets, which correspond to gymnastics, diving and figure skating, respectively. |
| Dataset Splits | No | The paper mentions training and testing, and refers to Appendix B and C for more details, but it does not explicitly state specific training, validation, or test dataset split percentages or sample counts in the main text. |
| Hardware Specification | No | The paper states that training details are in Appendix C, but the main text does not specify any particular hardware (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | The paper mentions using a Transformer Encoder, ResNet, OpenPose, and cites PyTorch, but it does not provide specific version numbers for any of these software components. |
| Experiment Setup | No | The paper describes the general architecture and some training concepts (e.g., triplet loss, reparameterization trick, using box embeddings), but it refers to Appendix C for more details and does not provide specific hyperparameter values like learning rates, batch sizes, or optimizer settings in the main text. |