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..
Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Authors: Ramesha Rakesh Mugaludi, Jogendra Nath Kundu, Varun Jampani, Venkatesh Babu R
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate our superiority against prior-arts in self-adapting a source trained model to diverse unlabeled target domains, such as a) in-the-wild datasets, b) low-resolution image domains, and c) adversarially perturbed image domains (via UAP). |
| Researcher Affiliation | Collaboration | Mugalodi Rakesh1 Jogendra Nath Kundu1 Varun Jampani2 R. Venkatesh Babu1 1Indian Institute of Science, Bangalore 2Google Research |
| Pseudocode | Yes | Algorithm 1: Proposed adaptation procedure. |
| Open Source Code | No | The paper provides a webpage link (https://sites.google.com/view/align-topo-human) but does not contain an explicit statement that the source code for the described methodology is released or available there. It is not a direct link to a code repository. |
| Open Datasets | Yes | Datasets. Moshed [37] CMU-Mo Cap [32] and H3.6M training-set [19] form our unpaired 3D pose data... We use SURREAL [57] to train our synthetic source model... We use a mixture of Human3.6M [19] and MPII [1] as our Real-domain dataset... We perturb the clean Human3.6M [19] samples... We use low-resolution (LR) variants of 3DPW [58] dataset... |
| Dataset Splits | Yes | We evaluate on Human3.6M [19] following Protocol-2 [23]. |
| Hardware Specification | Yes | A single iteration of the iterative ο¬tting procedure takes nearly 12ms on a Titan-RTX GPU. |
| Software Dependencies | No | The paper mentions software components like 'Res Net-50' and 'Adam optimizer' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We use the Adam optimizer [25] with a learning rate 1e 6 and batch size of 16, while setting Max Iteropt to 10 and K=4. |