Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation
This addresses face shape distortion in face re-enactment for computer vision applications, representing an incremental improvement through hybrid methods.
The paper tackles the problem of identity mismatch in one-shot face re-enactment by using a 3D Morphable Model for explicit facial semantic decomposition and dense correspondence estimation, achieving advantages over state-of-the-art benchmarks in identity preservation and re-enactment fulfillment.
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of driving subjects would inevitably interfere with the re-enactment results and lead to face shape distortion. To solve this problem, this paper proposes to use 3D Morphable Model (3DMM) for explicit facial semantic decomposition and identity disentanglement. Instead of using 3D coefficients alone for re-enactment control, we take the advantage of the generative ability of 3DMM to render textured face proxies. These proxies contain abundant yet compact geometric and semantic information of human faces, which enable us to compute the face motion field between source and driving images by estimating the dense correspondence. In this way, we could approximate re-enactment results by warping source images according to the motion field, and a Generative Adversarial Network (GAN) is adopted to further improve the visual quality of warping results. Extensive experiments on various datasets demonstrate the advantages of the proposed method over existing start-of-the-art benchmarks in both identity preservation and re-enactment fulfillment.