license: mit
tags:
- thispersondoesnotexist
- stylegan
- stylegan2
- mesh
- model
- 3d
- asset
- generative
pretty_name: HeadsNet
size_categories:
- 1K<n<10K
HeadsNet
This dataset uses the thispersondoesnotexist_to_triposr_6748_3D_Heads dataset as a foundation.
The heads dataset was collecting using the scraper Dataset_Scraper.7z based on TripoSR with this marching cubes improvement by thatname/zephyr which converts the 2D images from ThisPersonDoesNotExist to 3D meshes.
Vertex Normals need to be generated before we can work with this dataset, the easiest method to achieve this was with a simple Blender script:
import bpy
import glob
import pathlib
if not isdir(outputDir): mkdir(outputDir)
importDir = "ply/"
outputDir = "ply_norm/"
if not isdir(outputDir): mkdir(outputDir)
for file in glob.glob(importDir + "*.ply"):
model_name = pathlib.Path(file).stem
if pathlib.Path(outputDir+model_name+'.ply').is_file() == True: continue
bpy.ops.wm.ply_import(filepath=file)
bpy.ops.wm.ply_export(
filepath=outputDir+model_name+'.ply',
filter_glob='*.ply',
check_existing=False,
ascii_format=False,
export_selected_objects=False,
apply_modifiers=True,
export_triangulated_mesh=True,
export_normals=True,
export_uv=False,
export_colors='SRGB',
global_scale=1.0,
forward_axis='Y',
up_axis='Z'
)
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete(use_global=False)
bpy.ops.outliner.orphans_purge()
bpy.ops.outliner.orphans_purge()
bpy.ops.outliner.orphans_purge()
Importing the PLY without normals causes Blender to automatically generate them.
At this point the PLY files now need to be converted to training data, for this I wrote a C program DatasetGen_2_6.7z using RPLY to load the PLY files and convert them to binary data which I have provided here HeadsNet-2-6.7z.
It's always good to NAN check your training data after generating it so I have provided a simple Python script for that here nan_check.py.
This binary training data can be loaded into Python using:
load_x = []
with open("train_x.dat", 'rb') as f:
load_x = np.fromfile(f, dtype=np.float32)
load_y = []
with open("train_y.dat", 'rb') as f:
load_y = np.fromfile(f, dtype=np.float32)
The data can then be reshaped and saved back out as a numpy array which makes for faster loading:
inputsize = 2
outputsize = 6
train_x = np.reshape(load_x, [tss, inputsize])
train_y = np.reshape(load_y, [tss, outputsize])
np.save("train_x.npy", train_x)
np.save("train_y.npy", train_y)