RingNet | Regress 3D Face Shape and Expression from an Image | Computer Vision library
kandi X-RAY | RingNet Summary
kandi X-RAY | RingNet Summary
RingNet is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. RingNet has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However RingNet has 2 bugs. You can download it from GitHub.
This is an official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision. The project was formerly referred by RingNet. The codebase consists of the inference code, i.e. give an face image using this code one can generate a 3D mesh of a complete head with the face region. For further details on the method please refer to the following publication,. More details on our NoW benchmark dataset, 3D face reconstruction challenge can be found in our project page. A pdf preprint is also available on the project page.
This is an official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision. The project was formerly referred by RingNet. The codebase consists of the inference code, i.e. give an face image using this code one can generate a 3D mesh of a complete head with the face region. For further details on the method please refer to the following publication,. More details on our NoW benchmark dataset, 3D face reconstruction challenge can be found in our project page. A pdf preprint is also available on the project page.
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Quality
Security
License
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RingNet has a low active ecosystem.
It has 719 star(s) with 161 fork(s). There are 40 watchers for this library.
It had no major release in the last 6 months.
There are 19 open issues and 43 have been closed. On average issues are closed in 63 days. There are 4 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of RingNet is current.
Quality
RingNet has 2 bugs (0 blocker, 0 critical, 1 major, 1 minor) and 31 code smells.
Security
RingNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
RingNet code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
RingNet is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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RingNet releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
RingNet saves you 309 person hours of effort in developing the same functionality from scratch.
It has 743 lines of code, 39 functions and 14 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed RingNet and discovered the below as its top functions. This is intended to give you an instant insight into RingNet implemented functionality, and help decide if they suit your requirements.
- Render the image
- Render a mesh
- Render a scene
- Draws a rotated image
- Load a dynamic contour
- Compute 3D mesh points for a mesh
- Load an embedding file
- Scale and crop an image
- Resize image
- Generate a neutral mesh
- Prepare arguments for pickling
- Loads a Verts curve
- Return the position of a posemap
- Preprocess an image
- Predict from images
- Create a texture map
- Compute a texture map
Get all kandi verified functions for this library.
RingNet Key Features
No Key Features are available at this moment for RingNet.
RingNet Examples and Code Snippets
No Code Snippets are available at this moment for RingNet.
Community Discussions
Trending Discussions on RingNet
QUESTION
Stop compiler to suggest candidates for overloaded template, expand PRETTY_FUNCTION in static_assert
Asked 2020-Jun-13 at 07:00
My code use templates heavily. I have a number of overloaded functions
...ANSWER
Answered 2020-Jun-11 at 19:11#include
template
struct Type_Not_Supported : std::false_type {};
template
auto operator>>(byte_vector_view& bvv, UnknownType&&) -> byte_vector_view& {
static_assert(Type_Not_Supported{});
return bvv;
}
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install RingNet
The code uses Python 2.7 and it is tested on Tensorflow gpu version 1.12.0, with CUDA-9.0 and cuDNN-7.3.
Download pretrained RingNet weights from the project website, downloads page. Copy this inside the model folder
Download FLAME 2019 model from here. Copy it inside the flame_model folder. This step is optional and only required if you want to use the output Flame parameters to play with the 3D mesh, i.e., to neutralize the pose and expression and only using the shape as a template for other methods like VOCA (Voice Operated Character Animation).
Download the FLAME_texture_data and unpack this into the flame_model folder.
Download pretrained RingNet weights from the project website, downloads page. Copy this inside the model folder
Download FLAME 2019 model from here. Copy it inside the flame_model folder. This step is optional and only required if you want to use the output Flame parameters to play with the 3D mesh, i.e., to neutralize the pose and expression and only using the shape as a template for other methods like VOCA (Voice Operated Character Animation).
Download the FLAME_texture_data and unpack this into the flame_model folder.
Support
If you have any questions you can contact us at soubhik.sanyal@tuebingen.mpg.de and timo.bolkart@tuebingen.mpg.de.
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