Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity separation mechanisms often leads to identity confusion in multi-identity settings. Existing multi-identity approaches, which directly extend single-identity frameworks by concatenating face images as input conditions, frequently result in unnatural facial expressions and motions, manifesting as the "copy-paste" phenomenon.
arXiv:2601.01352v2 Announce Type: replace
Abstract: Human identity-preserving text-to-video generation remains challenging under large changes in viewpoint, facial expression, illumination, and motio...
By Yixuan Lai, He Wang, Kun Zhou, Tianjia Shao
Face Video Restoration (FVR) aims to recover high-fidelity facial videos from degraded input while preserving identity and semantic consistency across frames. Existing methods often struggle to simultaneously address three key challenges: identity shift, viewpoint-entangled guidance, and perceptual realism.
arXiv:2607. 22830v2 Announce Type: replace Abstract: In visual storytelling, human performances are central to creative intent and narrative meaning.
By Yuancheng Xu, Mingming He, Pablo Salamanca, Li Ma, Yash Kant, Emmett Steven, Paul Debevec, Ning Yu
The paper addresses the challenge of identity‑preserving video generation when a single reference portrait cannot capture the subject’s appearance across varied expressions. It introduces a compact, expression‑diverse reference gallery and a new evaluation metric that measures identity similarity for mild, intense, and extreme expressions. By extending the Stand‑In model to use these galleries and curating diverse face crops from training videos, the authors demonstrate improved identity preservation, especially for extreme expressions, on both real and synthesized reference sets.
By Tianwen Fu, Wenbin Teng, Gonglin Chen, Junyi Ouyang, Haolin Xiong, Yajie Zhao
arXiv:2606. 06903v1 Announce Type: cross Abstract: Human image animation aims to generate a video from a static reference image, guided by pose information extracted from a driving video.
By Yuan Zeng, Yujia Shi, Yuhao Yang, Dongxia Liu, Zongqing Lu, Wenming Yang, Qingmin Liao