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..
Continuous Surface Embeddings
Authors: Natalia Neverova, David Novotny, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that the proposed approach performs on par or better than the state-of-the-art methods for dense pose estimation for humans, while being conceptually simpler. We also collect a new in-the-wild dataset of dense correspondences for animal classes and demonstrate that our framework scales naturally to the new deformable object categories. 5 Experiments. The experimental results are reported following the updated protocol based on GPSm scores [54]. |
| Researcher Affiliation | Industry | Natalia Neverova David Novotny Vasil Khalidov Marc Szafraniec Patrick Labatut Andrea Vedaldi {nneverova, dnovotny, vkhalidov, mszafraniec, plabatut, vedaldi} fb.com. Facebook AI Research |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The code, trained models and the dataset will be made publicly available to ensure reproducibility. |
| Open Datasets | Yes | We rely on the Dense Pose-COCO dataset [17] for evaluation of the proposed method on the human category and comparison with the Dense Pose (IUV) training. Additionally, we collect correspondence annotations on a set of 9 animal categories of the LVIS dataset [21]. Based on images from the COCO dataset [31], LVIS features significantly more accurate object masks. |
| Dataset Splits | Yes | The training is performed on 8 GPUs for 130k iterations on Dense Pose-COCO (standard s1x schedule [54]) and 5k iterations on Dense Pose-Chimps and Dense Pose-LVIS. Table 2: Performance on Dense Pose-COCO, with IUV (top) and CSE (bottom) training (GPSm scores, minival). Table 3: Hyperparameter search and performance in low data regimes (AP, Dense Pose COCO, minival). |
| Hardware Specification | No | The training is performed on 8 GPUs. This is a general statement and does not specify the model or type of GPUs, or any other hardware components. |
| Software Dependencies | No | Our networks are implemented in PyTorch within the Detectron2 [54] framework. The paper mentions software names but does not provide specific version numbers for reproducibility. |
| Experiment Setup | Yes | The training is performed on 8 GPUs for 130k iterations on Dense Pose-COCO (standard s1x schedule [54]) and 5k iterations on Dense Pose-Chimps and Dense Pose-LVIS. All CSE models are trained with loss LĪ (eq. 4), LBO size M = 256, embedding size D = 16. |