1,721,143 research outputs found
Synthetic data (Part 2) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
No full textHere we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset.
The overall structure of the data is:
├── render_sdf_ho3d.zip - Contains the rendered images for HO3Dv2.
If you find our code, weights, predictions or ideas useful, please cite:
@inproceedings{qi2024hoisdf, title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields}, author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={10392--10402}, year={2024}}UPAMATHISLCAVSDSC-GE2.
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
No full textHere we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here.
The overall structure of the data is:
├── render_sdf_ho3d.zip - Contains the processed SDF files for HO3Dv2 rendered images.
├── train_ho3d.zip - Contains the processed SDF files for HO3Dv2 training set.
├── full_test_dexycb.zip - Contains the processed SDF files for DexYCB full test set.
The code to reproduce the results is available at: https://github.com/amathislab/HOISDF
--------------------------------
If you find our code, weights, predictions or ideas useful, please cite:
@inproceedings{qi2024hoisdf, title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields}, author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={10392--10402}, year={2024}}UPAMATHISLCAVSDSC-G
Acquiring musculoskeletal skills with curriculum-based reinforcement learning - model weights
No full textHere we provide the weights of the neural network policies used for the analysis presented in our article.
The archives whose names start with a number (01 - 32) correspond to the 32 curriculum steps to train the Baoding Balls policy which ranked first at the MyoChallenge 2022. The code used for the training and which can be used to test the policies can be found at https://github.com/amathislab/myochallenge.
The archives hand_pose, hand_reach, pen and reorient correspond to the other policies used in the article. They were developed in the paper Latent exploration for reinforcement learning, Chiappa et al., NeurIPS 2023. They can be loaded and tested with the code at https://github.com/amathislab/lattice.
The archive datasets includes three subfolders: rollouts, umap and csi.
The files in rollouts are the datasets of transitions resulting from the interaction between a policy and the environment.
The files in umap are the pre-computed projections of specific subsets fo the datasets included in rollouts using UMAP.
The files in csi report the performance of the policies described in our paper when applying Control Subspace Inactivation (CSI).
These datasets are necessary to run the notebooks to reproduce the paper's figures and main results, with the code at https://github.com/amathislab/MyoChallengeAnalysis
If you find these weights useful, please cite:
@article{chiappa2024acquiring,
title={Acquiring musculoskeletal skills with curriculum-based reinforcement learning},
author={Chiappa, Alberto Silvio and Tano, Pablo and Patel, Nisheet and Ingster, Abigail and Pouget, Alexandre and Mathis, Alexander},
journal={bioRxiv},
pages={2024--01},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}
@article{chiappa2024latent,
title={Latent exploration for reinforcement learning},
author={Chiappa, Alberto Silvio and Marin Vargas, Alessandro and Huang, Ann and Mathis, Alexander},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}UPAMATHIS2.
Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders (BehaveMAE & Shot7M2)
No full textElucidating the Hierarchical Nature of Behavior with Masked Autoencoders
Authors: Lucas Stoffl, Andy Bonnetto, Stéphane D'Ascoli & Alexander Mathis
Affiliation: Ecole Polytechnique de Lausanne (EPFL)
Date: 25/09/2024
Link to the BiorXiv article : https://doi.org/10.1101/2024.08.06.606796
-----------------
Provided data (hBehaveMAE checkpoints)
We provide a collection of pre-trained models that were reported in our paper, allowing you to reproduce our results for MABe22, hBABEL and Shot7M2 datasets.
Note that you can download Shot7M2 on HuggingFace and generate hBABEL by following the instructions on the github page.
hBehaveMAE_hBABEL.pth : checkpoint for the hBehaveMAE pre-trained on the hBABEL dataset
hBehaveMAE_Shot7M2.pth : checkpoint for the hBehaveMAE pre-trained on the Shot7M2 dataset
hBehaveMAE_MABe22.pth: checkpoint for the hBehaveMAE pre-trained on the MABe22 dataset
References
If you find our code, weights or ideas useful, please cite:
@article {Stoffl2024hBehaveMAE, author = {Stoffl, Lucas and Bonnetto, Andy and d{\textquoteright}Ascoli, St{\'e}phane and Mathis, Alexander}, title = {Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders}, elocation-id = {2024.08.06.606796}, year = {2024}, doi = {10.1101/2024.08.06.606796}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796}, eprint = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796.full.pdf}, journal = {bioRxiv}}UPAMATHISv
HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models
No full textHOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024
Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.
Affiliation: EPFL
Date: June, 2024
Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf
Link to the Arxiv article: https://arxiv.org/abs/2402.17062
--------------------------------
Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.
The overall structure of the data is:
├── ckpts.zip - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)
├── annotations.zip - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.
├── simple_ycb_models.zip - Contains the preprocessed YCB objects for batched evaluation.
├── test.zip - Contains the processed SDF files for DexYCB test set.
The code to reproduce the results is available at: https://github.com/amathislab/HOISDF
--------------------------------
If you find our code, weights, predictions or ideas useful, please cite:
@inproceedings{qi2024hoisdf, title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields}, author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={10392--10402}, year={2024}}CVLABUPAMATHIS
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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