#مقاله #سورس_کد
#CVPR2019 #face
Learning to Regress 3D Face Shape and Expressionfrom an Image without 3D Supervision
Get 3D faces from an image using RingNet. New CVPR paper with code on-line. The output is a 3D face/head model that can be animated. We do this with paired 3D-image training data.
#Video:
https://youtu.be/6wPQaJBgreE
#Source Code (#Tensorflow):
https://github.com/soubhiksanyal/RingNet
#Project_page:
https://ringnet.is.tue.mpg.de/
paper:
https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/509/paper_camera_ready.pdf
#CVPR2019 #face
Learning to Regress 3D Face Shape and Expressionfrom an Image without 3D Supervision
Get 3D faces from an image using RingNet. New CVPR paper with code on-line. The output is a 3D face/head model that can be animated. We do this with paired 3D-image training data.
#Video:
https://youtu.be/6wPQaJBgreE
#Source Code (#Tensorflow):
https://github.com/soubhiksanyal/RingNet
#Project_page:
https://ringnet.is.tue.mpg.de/
paper:
https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/509/paper_camera_ready.pdf
YouTube
RingNet: Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a large training set of in-the-wild images, which by construction, lack ground truth…