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Related Experiment Video

Updated: Jan 21, 2026

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On Learning 3D Face Morphable Model from In-the-Wild Images.

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    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 23, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a novel nonlinear 3D Morphable Model (3DMM) learned from in-the-wild images, overcoming limitations of traditional 3DMMs. The new model enhances 3D face analysis tasks like reconstruction and editing.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Traditional 3D Morphable Models (3DMMs) are limited by linear bases and require 3D face scans.
    • Existing 3DMMs struggle with the diversity and scale of real-world facial data.
    • Linear PCA-based 3DMMs have constrained representation power for complex facial variations.

    Purpose of the Study:

    • To develop a nonlinear 3DMM capable of learning from large-scale, in-the-wild 2D face images without 3D scans.
    • To improve the representation power and applicability of 3DMMs in facial analysis.
    • To enable end-to-end training with weak supervision for 3D face modeling.

    Main Methods:

    • A novel framework using a network encoder-decoder architecture for nonlinear 3DMM learning.
    • An encoder estimates facial parameters (projection, lighting, shape, albedo) from input images.
    • Two decoders generate 3D shape and albedo, coupled with a differentiable rendering layer for reconstruction.

    Main Results:

    • The proposed nonlinear 3DMM demonstrates superior representation power compared to linear counterparts.
    • The model effectively handles in-the-wild facial images, improving 3D face reconstruction.
    • Significant contributions were observed in face alignment, 3D reconstruction, and face editing tasks.

    Conclusions:

    • The nonlinear 3DMM framework offers a more robust and powerful statistical model for 3D face analysis.
    • Learning from unconstrained 2D images significantly expands the applicability of 3DMMs.
    • This approach advances the state-of-the-art in 3D face modeling and analysis from real-world data.