3D Human Face Reconstruction with 3DMM face model from RGB image
TLDR
This paper introduces a pipeline for 3D human face reconstruction from a single RGB image, addressing data limitations for CNNs.
Key contributions
- Presents a pipeline for 3D human face reconstruction from a single RGB image.
- Addresses the challenge of generating detailed, photo-realistic data for CNN training.
- Integrates face detection, landmark detection, 3DMM parameter regression, and soft rendering.
Why it matters
This paper is significant as it provides a practical pipeline for reconstructing detailed 3D human faces from just one RGB image. This approach helps overcome the limitations of generating photo-realistic labeled data for CNNs, which is crucial for advancements in AR/VR, gaming, and virtual try-on applications.
Original Abstract
Nowadays as convolution neural networks demonstrate its powerful problem-solving ability in the area of image processing, efforts have been made to reconstruct detailed face shapes from 2D face images or videos. However, to make the full use of CNN, a large number of labeled data is required to train the network. Coarse morphable face model has been used to synthesize labeled data. However, it is hard for coarse morphable face models to generate photo-realistic data with detail such as wrinkles. In this project, we present a pipeline that reconstructs a human face 3D model from a single RGB image. The pipeline includes face detection, landmark detection, regression of 3DMM model parameters, and soft rendering. Mentor: Zhipeng Fan (Email: zf606@nyu.edu) Code Repository: https://github.com/SeVEnMY/3d-face- reconstruction Code Reference: https://github.com/sicxu/Deep3DFaceRecon pytorch
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