arXiv:2011.00269
공식 코드 · Papers with Code
LandmarkGAN: Synthesizing Faces from Landmarks
원문 초록
Face synthesis is an important problem in computer vision with many applications. In this work, we describe a new method, namely LandmarkGAN, to synthesize faces based on facial landmarks as input. Facial landmarks are a natural, intuitive, and effective representation for facial expressions and orientations, which are independent from the target's texture or color and background scene. Our method is able to transform a set of facial landmarks into new faces of different subjects, while retains the same facial expression and orientation. Experimental results on face synthesis and reenactments demonstrate the effectiveness of our method.
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FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric Guidance
ID-Booth: Identity-consistent Face Generation with Diffusion Models
FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis
토론
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