APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals
원문 초록
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However, existing methods can not generate vivid face images or only reenact low-resolution faces, which limits the application value. To solve those problems, we propose a novel deep neural network named APB2Face, which consists of GeometryPredictor and FaceReenactor modules. GeometryPredictor uses extra head pose and blink state signals as well as audio to predict the latent landmark geometry information, while FaceReenactor inputs the face landmark image to reenact the photorealistic face. A new dataset AnnVI collected from YouTube is presented to support the approach, and experimental results indicate the superiority of our method than state-of-the-arts, whether in authenticity or controllability.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric Guidance
StableAnimator: High-Quality Identity-Preserving Human Image Animation
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
DF40: Toward Next-Generation Deepfake Detection
AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation
Deepfake Generation and Detection: A Benchmark and Survey
토론
이 논문과 연결된 포럼 글이 아직 없습니다.