ICON
lightweight-human-pose-estimation.pytorch
| ICON | lightweight-human-pose-estimation.pytorch | |
|---|---|---|
| 6 | 2 | |
| 1,542 | 2,021 | |
| - | - | |
| 4.1 | 0.0 | |
| 5 months ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ICON
- ControlNet fully integrated with Blender using nodes!
- Is there any AI that can compile several pictures of a person into a single, 3d version?
-
[R][P] ICON: Implicit Clothed humans Obtained from Normals + Gradio Web Demo
github: https://github.com/YuliangXiu/ICON
- Show HN: Icon-3D Avatar Creator from 2D Pixels
-
Icon: Towards Large-Scale Avatar Creation from In-the-Wild Pixels
Realistic virtual humans will play a central role in mixed and augmented reality, forming a critical foundation for the Metaverse and supporting remote presence, collaboration, education, and entertainment.
To enable this, new tools are needed to easily create large-scale 3D virtual humans that can be readily animated. However, current methods need either posed 3D scans captured by expensive scanning equipment or 2D images with carefully controlled user poses. Both of them can't scale up easily.
ICON ("Implicit Clothed humans Obtained from Normals") takes a step towards robust 3D clothed human reconstruction from in-the-wild images. This also enables creating animatable avatars directly from video with personalized and natural pose-dependent cloth deformation.
Homepage: https://icon.is.tue.mpg.de/
Github: https://github.com/YuliangXiu/ICON
Google Colab: https://colab.research.google.com/drive/1-AWeWhPvCTBX0KfMtgt...
lightweight-human-pose-estimation.pytorch
-
Help finding an appropriate model for human pose estimation
Lightweight OpenPose: Runs in realtime >20fps confirmed, training code is provided
-
How do I properly dissect a Github repo of a ML model?
Using https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch as an example (or another repo, that was just one I found), could someone please give me a step by step process of how they read a repo for a research paper?
What are some alternatives?
ECON - [CVPR'23, Highlight] ECON: Explicit Clothed humans Optimized via Normal integration
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
dream-textures - Stable Diffusion built-in to Blender
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
text2cinemagraph - Text2Cinemagraph: Text-Guided Synthesis of Eulerian Cinemagraphs [SIGGRAPH ASIA 2023]
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
aistplusplus_api - API to support AIST++ Dataset: https://google.github.io/aistplusplus_dataset
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
VIBE - Official implementation of CVPR2020 paper "VIBE: Video Inference for Human Body Pose and Shape Estimation"
kapao - KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.