FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation

FaithfulFaces introduces a pose‑faithful facial identity preservation framework for text‑to‑video generation, addressing identity distortion caused by large pose variations or occlusions. The method employs a pose‑shared identity aligner that refines and aligns facial poses across views using a pose‑shared dictionary and a pose‑variation‑identity invariance constraint. By mapping single‑view inputs into a global pose representation with explicit Euler angle embeddings, the framework provides a robust facial prior that guides generation toward consistent identity and structural clarity, achieving state‑of‑the‑art performance on a curated high‑quality video dataset.