Lifting-from-the-Deep-release
PyMAF
Lifting-from-the-Deep-release | PyMAF | |
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2 | 1 | |
448 | 581 | |
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0.0 | 3.6 | |
over 2 years ago | 4 months ago | |
Python | Python | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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Lifting-from-the-Deep-release
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Auto tagging Images ( particularly for p0rn ) scene-wise
Found relevant code at https://github.com/DenisTome/Lifting-from-the-Deep-release + all code implementations here
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How do I install deeplearning package from github and apply transfer learning on it?
You should try reading the source code of the bash script and the other source files to get a sense of what they do and how you can incorporate it into your project. Here's some tips to get started (assuming you're looking at this repo: https://github.com/DenisTome/Lifting-from-the-Deep-release)
PyMAF
What are some alternatives?
3DMPPE_POSENET_RELEASE - Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019
metrabs - Estimate absolute 3D human poses from RGB images.
lightweight-human-pose-estimation.pytorch - Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
DeepHuman - Code for our ICCV paper "DeepHuman: 3D Human Reconstruction from a Single Image"
miles-deep - Deep Learning Porn Video Classifier/Editor with Caffe
HuManiFlow - [CVPR 2023] Code repository for HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation