for customized data preprocessing, how to generate the required shape expression vector?

[Qing Yang (Charles)]

@gafniguy
as this vht repo "https://github.com/philgras/video-head-tracker", it outputs totally different dimension of expression vector (which is 100d), how to align this with nerface requirement (expression is 76D vector)?

Also, again, how to generate the rigid.txt, transform.txt needed by real_to_nerf.py? could you provide samples?
"
expressions = read_expressions(os.path.join(args.source, "expression.txt"))
rigid_poses, scale = read_rigid_poses(os.path.join(args.source, "rigid.txt"))
"