Fitting Facial Models to Spatial Points: Blendshape Approaches and Benchmark
Taelim Choi, Jiwoo Kang, Hyewon Song, Sanghoon Lee · 2018
Blendshape is one of the most common facial representation used for 3D animation, 3D game and virtual reality. In this paper, four representative blendshape approaches are benchmarked: global, delta, mean-delta, and SVD-based blend-shapes. When fitting the blendshape models to sparse facial points, the obtained facial shape highly depends on fitting approach due to the lack of the fitted points. Therefore, it is important to set up appropriate criteria for comparing and verifying the performance of the approaches. In this paper, we use four kinds of metrics that are utilized to measure the performance of the approaches: fitting, landmark, and vertex errors and coefficient sparsity. Through the experimental results, it is verified that the benchmarks are very effective to measure the subjective quality of blendshape.