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: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
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
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:2610.35414v1 Announce Type: new
Abstract: Photorealistic avatar videos decouple the appearance of a rendered face from the person who operates it, which makes conventional face recognition unsu...
By Katsuya Ogata, Ryoya Yoshiyama, Kawachi Hodaka, Yuta Nakashima
Vorch-Human is a unified framework for human‑centric audio‑visual generation that handles multiple tasks—animating a person from speech, jointly generating speech and video from a voice reference, and synthesizing a scene from paired appearance and voice references—using a single dual‑stream audio‑video diffusion transformer. The model incorporates clean condition‑audio and condition‑video tokens, per‑token task embeddings, temporal position types, condition masks, and a shared multimodal prompt encoder to express diverse inputs such as driving speech, timbre examples, first frames, and subject images. A two‑level data pipeline supplies the necessary supervision by extracting speech, appearance, and timbre annotations from clips and linking consistent identity and outfit references across videos, while a frozen‑prefix recurrence enables long‑form audio‑driven generation with reduced boundary discontinuity and identity drift.
By Yang Ding, Haoran Yu, Xin Ma, Yulei Lu, Menglin Han, Yaole Wang, Siqian Yang, Gang Yue, Kaihao Zhang, Yaohui Wang, Lin Ma
arXiv:2606. 11670v1 Announce Type: cross Abstract: Subject-preserving video generation is not solved by frontal-face similarity alone: a generated person must remain recognizable across motion, large viewpoint changes, expression shifts, occlusion, scale variation, and conflicts among text, first-frame, and identity references.
By Zijie Meng, Jiwen Liu, Yufei Liu, Chengzhuo Tong, Xiaoqiang Liu, Yuanxing Zhang, Yulong Xu, Pengfei Wan
The paper introduces an end‑to‑end framework for personalized audio‑driven facial motion that operates in real time without look‑ahead. It combines a causal multi‑resolution motion tokenizer, which captures both global temporal context and fine articulatory details, with a multi‑modal style retriever that pulls stylistic priors from arbitrary reference footage using ongoing audio and motion queries. This approach allows high‑fidelity, identity‑consistent animation from just a few casually recorded clips, outperforming existing methods in lip‑sync accuracy, identity consistency, and perceived realism while maintaining real‑time streaming constraints.
By Xuangeng Chu, Yu Han, Wei Mao, Shih-En Wei
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.
By Yuanzhi Wang, Xuhua Ren, Jiaxiang Cheng, Bing Ma, Kai Yu, Sen Liang, Wenyue Li, Tianxiang Zheng, Qinglin Lu, Zhen Cui
arXiv:2607. 16287v1 Announce Type: cross Abstract: Neural Radiance Fields (NeRF) have enabled photorealistic novel-view synthesis of 3D scenes and, in the facial domain, have been extended to reconstruct and animate 3D face models from a small number of images.
By Minh Tran
The paper presents KVeil, the first‑place solution to the ECCV 2026 LifeGenIP Challenge on Unlearnable Videos. It introduces Reference Capacity Collapse and a Motion‑Guided EOT mechanism for Phase 1, and a two‑layer framework that protects facial and background regions for Phase 2, achieving strong protection against various customization attacks. The methods build on the authors’ prior work LETHE and are publicly available on GitHub.
By Boheng Li, Yueming Xing, Yiming Li, Jie Zhang, Han Qiu, Tianwei Zhang
arXiv:2601. 00664v2 Announce Type: replace-cross Abstract: Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation.
By Taekyung Ki, Sangwon Jang, Jaehyeong Jo, Jaehong Yoon, Sung Ju Hwang