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- W4286324963 abstract "The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists of 100 globally aligned face scans with accurate facial keypoints, high-quality region masks, and topology-consistent meshes. Our approach performs region-wise shape alignment and leads to more accurate, bidirectional correspondences during computing the shape errors. The fine-grained, region-wise evaluation results provide us detailed understandings about the performance of state-of-the-art 3D face reconstruction methods. For example, our experiments on single-image based reconstruction methods reveal that DECA performs the best on nose regions, while GANFit performs better on cheek regions. Besides, a new and high-quality 3DMM basis, HIFI3D++, is further derived using the same procedure as we construct REALY to align and retopologize several 3D face datasets. We will release REALY, HIFI3D++, and our new evaluation pipeline at https://realy3dface.com." @default.
- W4286324963 created "2022-07-21" @default.
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- W4286324963 date "2022-03-18" @default.
- W4286324963 modified "2023-09-30" @default.
- W4286324963 title "REALY: Rethinking the Evaluation of 3D Face Reconstruction" @default.
- W4286324963 doi "https://doi.org/10.48550/arxiv.2203.09729" @default.
- W4286324963 hasPublicationYear "2022" @default.
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