Towards Fast, Accurate and Stable 3D Dense Face Alignment

Existing methods of 3D dense face alignment mainly concentrate on accuracy, thus limiting the scope of their practical applications. In this paper, we propose a novel regression framework which makes a balance among speed, accuracy and stability... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Face Alignment AFLW 3DDFA_V2 Mean NME 4.43 # 1
Face Alignment AFLW2000-3D 3DDFA_V2 Mean NME 3.51% # 2
3D Face Reconstruction Florence 3DDFA_V2 Mean NME 3.56% # 1
3D Face Reconstruction NoW Benchmark 3DDFA_V2 Mean Reconstruction Error (mm) 1.57 # 3
3D Face Reconstruction Stirling-HQ (FG2018 3D face reconstruction challenge) 3DDFA_V2 Mean Reconstruction Error (mm) 1.91 # 2
3D Face Reconstruction Stirling-LQ (FG2018 3D face reconstruction challenge) 3DDFA_V2 Mean Reconstruction Error (mm) 2.10 # 3

Methods used in the Paper