I am looking for an approach to transform a 2D human head into 3D model and then animate its speech based on an audio file.
I tried some approaches such as meshTalks but the approach uses a pre generated 3d human head meshes.
Any help would be appreciated.
Thank you
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I'm working on this paper in which I'm simulating Vibration of Pipe Elbows with FEM, originally I made this in Matlab but then I migrated to Python.
Basically my data is stored in nodes, like this, where I have the coordinates of the node on the centerline of the pipe and the coordinates of the centerline of the cross-section.
I would like to use these data to make a smooth surface plot like this other one from this paper. But I'm not really sure about what function of Matplotlib I could use to work this out.
Somebody has an idea for this?
Hey everyone I'm pretty new to graphics programming and I want some guidance or working samples, I want to make any 3D objects but for starters I want to make a 3D cube using triangle mesh with given coordinates via python, and export all the mesh in an .obj file so I can load the mesh and view it in meshlab.
I would really love some guidance on this thanks.
if you are comfortable with meshlab you could find useful pymeshlab
https://pymeshlab.readthedocs.io
Here is a simple tutorial on how to create a mesh from just array of coords and indexes...
https://pymeshlab.readthedocs.io/en/latest/tutorials/import_mesh_from_arrays.html
I'm working with 3D meshes using vtk. I want to apply the scale transformation filter but it works holding, as a reference, the origin of the mesh. Now I would like to apply the same transformation but using a different fixed point.
The idea is to change the origin of the mesh to that point and than to apply the filter. Is it possible in vtk or with other libraries? Is there another method that works better?
I attach here two images that explain the problem.
Thank you in advance!
I couldn't find the proper answer to my problem on the Web, so I'll ask it here. Let's say we're given two 2D photos of the same place taken from slightly different angles. I've chosen the set of points (edge detection), found correspondences between them (which point is which on other photo). Now I need to somehow find out world coordinates of these points in 3D.
For the last 5 hours I've read a lot about it but I still can't understand what steps should I follow. I've tried to estimate motion of a camera using the function recoverPose applied to an essential matrix and two sets of points on each frame. I can't understand what it gives me when I know rotation and translation matrices (thatrecoverPose returned). What should I do in order to achieve my goal?
I also know the calibration matrix of my camera (I use KITTI dataset). I've read opencv documentation but still don't understand.
It's monocular vision.
I've been trying to read through the Stackoverflow questions for generating point clouds (x,y,z) coordinates from a left and right stereo image pair.
I haven't come to any definite solution, and I'm asking the community here for some help.
Problem statement: Given two stereo images, generate 3D (x,y,z) cartesian coordinate point clouds from those and do so in a way that lends itself to completing this point-cloud generation in a way that could work over a large set (thousands) of pairs of stereo images
My programming language experience lends itself to MATLAB, but I've dabbled in Python, and C++ is limited, but I may be able to work in that as well.
Speed is a factor here, so the the idea is to find a quick method of successively going through these pairs and generating the point cloud.
NOTE: I am not asking for the BEST as to avoid comparative solutions, I'm just asking for solutions.
Thank you very much!
Edit: After being recommended to utilize the Stereo Block Matching implementation in OpenCV, I may steer away from this method, as there is a wide variation in regards to texture of the topographical pictures that I'm dealing with.
What you probably want is called "Stereo Block Matching". If you don't feel like writing it yourself, OpenCV has an implementation ready to go (even a CUDA accelerated version).