Sure. The basic script we provide for generating the video rendering paths is imgs2renderpath.py. This is a wrapper around the function generate_render_path, that deals with loading the input view poses, creating the output path, and saving it to a text file ready for our CUDA renderer or the script mpis2video.py. So I would recommend making a new function to take the place of generate_render_path to do a custom path.

First thing to note, about our pose matrix convention: our Tensorflow graph expects poses with rotation matrices in the form [down, right backwards], but this line in imgs2renderpath.py changes them to the more traditional [right, up, backwards] since that is what the rendering code expects.

To explain some of the functions in llff.math.pose_math:

poses_avg(...) takes a list of poses and returns a central average pose that can be used as an "origin" for centering the whole pose path
viewmatrix(...) will create a new pose matrix from a z-axis (camera axis) direction, up vector, and camera position
render_path_axis(...) renders a linear motion along axis ax with the camera rotated to point at a distance focal along the z axis
render_path_spiral(...) renders an elliptical motion in the XY plane combined with a sinusoidal motion in the Z axis

Generally it is easiest to calculate a series of look-directions (z-axis/camera-axis), up vectors, and camera origins, then use viewmatrix to turn these into poses and save them out. Note that you need to keep around the hwf vector that encodes height, width, and focal length, and concatenate it on the end of your pose matrices like np.concatenate([viewmatrix(z, up, c), hwf], 1) to get a 3x5 matrix for each new view.

View poses rendering #19

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