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We present a novel end-to-end identity-agnostic face reenactment system,MaskRenderer, that can generate realistic, high fidelity frames in real-time.Although recent face reenactment works have shown promising results, there arestill significant challenges such as identity leakage and imitating mouthmovements, especially for large pose changes and occluded faces. MaskRenderertackles these problems by using (i) a 3DMM to model 3D face structure to betterhandle pose changes, occlusion, and mouth movements compared to 2Drepresentations; (ii) a triplet loss function to embed the cross-reenactmentduring training for better identity preservation; and (iii) multi-scaleocclusion, improving inpainting and restoring missing areas. Comprehensivequantitative and qualitative experiments conducted on the VoxCeleb1 test set,demonstrate that MaskRenderer outperforms state-of-the-art models on unseenfaces, especially when the Source and Driving identities are very different.

MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment