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Towards Fast, Accurate and Stable 3D Dense Face Alignment

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arxiv 2009.09960 v2 pith:PJPOURHC submitted 2020-09-21 cs.CV

Jianzhu Guo , Xiangyu Zhu , Yang Yang , Fan Yang , Zhen Lei , Stan Z. Li This is my paper · ORCID
classification cs.CV
keywords accuracyfacestabilityalignmentddfa-v2densemethodmodels
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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 named 3DDFA-V2 which makes a balance among speed, accuracy and stability. Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously. To further improve the stability on videos, we present a virtual synthesis method to transform one still image to a short-video which incorporates in-plane and out-of-plane face moving. On the premise of high accuracy and stability, 3DDFA-V2 runs at over 50fps on a single CPU core and outperforms other state-of-the-art heavy models simultaneously. Experiments on several challenging datasets validate the efficiency of our method. Pre-trained models and code are available at https://github.com/cleardusk/3DDFA_V2.

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Forward citations

Cited by 4 Pith papers

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  2. Generative Face Parsing Map Guided 3D Face Reconstruction Under Occluded Scenes

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A parsing-map-plus algorithm merges a face segmentation with a landmark-derived map to synthesize an un-occluded face, which then drives 3DMM-based 3D face reconstruction.

  3. 3D Face Reconstruction With Geometry Details From a Single Color Image Under Occluded Scenes

    cs.CV 2024-12 reject novelty 4.0 of 10

    A single-image face reconstruction pipeline that removes eyeglasses by face parsing and inpainting, then adds a learned bump map to a 3D morphable model for geometry details.

  4. Generative Landmarks Guided Eyeglasses Removal 3D Face Reconstruction

    cs.CV 2024-12 reject novelty 2.0 of 10

    A landmark-guided GAN deletes and repaints eyeglasses regions, then a ResNet-50 regresses 3DMM coefficients from the cleaned face to produce a glasses-free 3D reconstruction.

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