arXiv:1112.02095
Real-time face swapping as a tool for understanding infant self-recognition
원문 초록
To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos. We present a non-constraint face-swapper based on 3D visual tracking that achieves real-time performance through parallel computing. Our imitator system is par- ticularly suited for experiments involving children with Autistic Spectrum Disorder who are often strongly disturbed by the constraints of other methods.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection
R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
Post-training for Deepfake Speech Detection
Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking
Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
토론
이 논문과 연결된 포럼 글이 아직 없습니다.