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We propose to address the face reconstruction in the wild by using a multi-metric regression network, MMFace, to align a 3D face morphable model (3DMM) to an in-put image. The key idea is to utilize a volumetric sub-network to estimate an intermediate geometry representa-tion, and a parametric sub-network to regress the 3DMM parameters.

We support a wide spectrum of mainstream pose analysis tasks in current research community, including 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, 3d human mesh recovery, fashion landmark detection and animal pose estimation.

2019年6月1日 · To tackle this problem, we proposed a novel end-to-end 3D face reconstruction network consisting of a conditional GAN (cGAN) for cross-domain face synthesis and a novel mesh transformer for...

本文提出了一种从素描草图中重建具有可控纹理的3D人脸的新方法，称为S2TD-Face（Sketch to controllable Textured and Detailed Three-Dimensional Face）。S2TD-Face引入了一个两阶段形状重建框架，能够直接从输入草图中重建精细的带纹理的三维人脸形状。

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Reconstructing 3d face model with associated expression deformation from a single face image via constructing alow-dimensional expression deformation manifold. IEEE Trans. Pattern Anal. Mach. Intell., 33(10):2115–2121, 2011.

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This repository is the official implementation of S2TD-Face in ACM-MM 2024. S2TD-Face can reconstruct controllable textured and detailed 3D faces from sketches.

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