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..
Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Authors: Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, Venkatesh Babu R
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate different self-adaptation settings and demonstrate state-of-the-art 3D human pose estimation performance on standard benchmarks. |
| Researcher Affiliation | Collaboration | 1Indian Institute of Science, Bangalore 2Google Research |
| Pseudocode | Yes | Algorithm 1: Overview of the optimization steps. |
| Open Source Code | Yes | Webpage: https://sites.google.com/view/sa3dhp |
| Open Datasets | Yes | We use the CMU-Mo Cap [1] dataset as the sample set for unpaired 3D poses Y and unpaired pose sequences e Y. We use the synthetic SURREAL (S) dataset [79] as one of the source datasets. ... For a fair evaluation, we use the standard, in-studio Human3.6M (H) dataset [25] as both source or target domain, in different problem settings. |
| Dataset Splits | No | The paper mentions using different datasets for source and target domains (e.g., Human3.6M for source, MPI-INF-3DHP for target) and discusses 'unsupervised adaptation' and 'direct transfer' settings. However, it does not explicitly provide training/validation/test dataset splits with percentages, counts, or references to predefined splits for reproducibility within a single dataset. |
| Hardware Specification | Yes | The adaptation is performed on an Nvidia V100 GPU with each batch containing 8 videos each of frame-length 30 (see Suppl. for more details). |
| Software Dependencies | No | The paper mentions the use of 'Res Net-50', 'bidirectional LSTMs', and 'Adam optimizer' but does not provide specific version numbers for these components or the underlying deep learning framework used (e.g., TensorFlow, PyTorch). |
| Experiment Setup | Yes | The adaptation is performed on an Nvidia V100 GPU with each batch containing 8 videos each of frame-length 30 (see Suppl. for more details). ... We associate separate Adam optimizer [32] to each relational energy term which are optimized in alternate training iterations. ... The motion auto-encoder, {Em, Dm} is composed of bidirectional LSTMs [22] with 128 hidden units operating on a fixed sequence length of 30 (30 FPS)... |