Official repo for DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image (CVPR 2022).
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From scratch train the model with custom dataset. #20
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hewumars opened 1 year ago
I use fit_scan.py to generate my custom dataset.
after train 500 epoch, to test image, 2d vertices are close to ok, but 3d vertices are not correct at all.
What is the possible reason for this?
vertices unit is millimeters. label json is as follows: { "vertices": [ [ 0.04518344467567696, 0.17775802332478438, -0.39643736129255214 ], [ 0.048658336279237865, 0.1768529119593747, -0.39521232999733014 ], [ 0.04945889770001125, 0.17799078430798437, -0.39566139249431254 ], [ 0.04600878696145669, 0.17851796093268812, -0.39655851551505694 ], [ 0.049867298790216646, 0.18126493109592973, -0.3838844547280212 ], [ 0.05354209709941608, 0.18278256826344497, -0.3882279945523791 ], ........
], "model_view_matrix": [ [ 0.9792482433597487, 0.0477148473832626, 0.19696743694336385, 0.28946250677108765 ], [ -0.0029217173213029147, 0.9751122033902291, -0.22169270255772225, -0.300279438495636 ], [ -0.2026433849024943, 0.2165167063730747, 0.9550163215446188, 1.0343986749649048 ], [ 0.0, 0.0, 0.0, 1.0 ] ], "projection_matrix": [ [ 1373.6173986936753, 0.0, -252.10671060735615, 0.0 ], [ 0.0, 1372.9339350792657, 356.03684183339647, 0.0 ], [ 0.0, 0.0, 1, -0.2 ], [ 0.0, 0.0, 1, 0.0 ] ] }