Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..).
[NeurIPS 2024] Generalizable and Animatable Gaussian Head Avatar.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Model Development
Subcategory
Architecture
Keywords
3D, Computer vision
Stars
601
Forks
39
Contributors
1
Created date
2022-10-10
Updated date
2026-09-21
Stars in 1 day
0
Stars in 7 days
0
Rank
16,282
Previous rank
16,072
Rank change
-210
7-day stars rank
15,449
Previous 7-day stars rank
15,459
7-day stars rank change
10
Total stars rank
16,767
Previous total stars rank
16,741
Total stars rank change
-26
Country
Singapore
README summary
EVA3D: Compositional 3D Human Generation from 2D Image Collections EVA3D is a high-quality unconditional 3D human generative model that only requires 2D image collections for training. [02/2023] Training codes for DeepFashion with our processed dataset are released. [02/2023] Inference codes (512x256 generation on DeepFashion) are released, including colab and huggingface demos. [01/2023] EVA3D is accepted to ICLR 2023 (Spotlight):partying_face:! :love_you_gesture: Citation If you find our work useful for your research, please consider
citing the paper: :desktop_computer: Requirements NVIDIA GPUs are required for this project. We have test the inference codes on NVIDIA RTX2080Ti, NVIDIA V100, NVIDIA A100, NVIDIA T4. The training codes have been tested on NVIDIA V100, NVIDIA A100. We recommend using anaconda to manage the python environments. :running_woman: Inference Download Models The pretrain model and SMPL model are needed for inference. The following script downloads pretrain models. Put the downloaded models in the folder smpl_models. The folder
structure should look like Commands We provide a script for inference the model trained on DeepFashion, SHHQ, UBCFashion, AIST. :train: Training DeepFashion Download SMPL Models & Processed Datasets Commands Intermediate results will be saved under every 100 iterations. The first line presents inference images from EMA generator. The second line present one inference sample of the training generator and one sample from the training dataset. To inference the trained models, please refer to the **Inference** section. :newspaper_roll: License Distributed under
the S-Lab License. :raised_hands: Acknowledgements This study is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). This project is built on source codes shared by StyleSDF.
Frequently asked questions
How many stars and forks are recorded?
hongfz16/EVA3D has 601 stars and 39 forks.
What is the repository rank?
hongfz16/EVA3D is ranked 16,282.
What language and category are recorded?
hongfz16/EVA3D has 601 stars, uses Python and belongs to Model Development.
Which repository is closest in rank?
hongfz16/EVA3D is compared with google-deepmind/alphastar, ranked 16,281 with 601 stars.
README
EVA3D: Compositional 3D Human Generation from 2D Image Collections
[02/2023] Inference codes for SHHQ, UBCFashion and AIST are released.
[02/2023] Training codes for DeepFashion with our processed dataset are released.
[02/2023] Inference codes (512x256 generation on DeepFashion) are released, including colab and huggingface demos.
[01/2023] EVA3D is accepted to ICLR 2023 (Spotlight):partying_face:!
:love_you_gesture: Citation
If you find our work useful for your research, please consider citing the paper:
@inproceedings{
hong2023evad,
title={{EVA}3D: Compositional 3D Human Generation from 2D Image Collections},
author={Fangzhou Hong and Zhaoxi Chen and Yushi LAN and Liang Pan and Ziwei Liu},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=g7U9jD_2CUr}
}
:desktop_computer: Requirements
NVIDIA GPUs are required for this project.
We have test the inference codes on NVIDIA RTX2080Ti, NVIDIA V100, NVIDIA A100, NVIDIA T4.
The training codes have been tested on NVIDIA V100, NVIDIA A100.
We recommend using anaconda to manage the python environments.
The pretrain model and SMPL model are needed for inference.
The following script downloads pretrain models.
python download_models.py
Register and download SMPL models here. Put the downloaded models in the folder smpl_models. Only the neutral one is needed. The folder structure should look like
Intermediate results will be saved under checkpoint/train_deepfashion_512x256/volume_renderer/samples every 100 iterations. The first line presents inference images from EMA generator. The second line present one inference sample of the training generator and one sample from the training dataset.
To inference the trained models, please refer to the Inference section.
Support for more datasets coming soon...
:newspaper_roll: License
Distributed under the S-Lab License. See LICENSE for more information.
:raised_hands: Acknowledgements
This study is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).
This project is built on source codes shared by StyleSDF.
