arXiv AI
BEACON: Behavior and Appearance Control for Subject-Specific Video Generation
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arXiv Computer Vision
Oct 9
Expression-Diverse References for Identity-Preserving Video Generation
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.
arXiv Computer Vision
Sep 22
Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding
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...
arXiv Computer Vision
Aug 21
ID-V2V: Identity-Preserving Video Restylization
arXiv:2607. 22830v2 Announce Type: replace Abstract: In visual storytelling, human performances are central to creative intent and narrative meaning.
Hugging Face Trending Papers
Jul 23
GroupVideo: Multi-Identity Customized Text-to-Video Generation
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 Computer Vision
Oct 27
Unveil the Mask: Jointly-Optimized Diffusion Masked Pretraining for Appearance-Agnostic Operator Verification
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...
arXiv Computer Vision
Sep 23
Vorch-Human: Unified Multi-Task Human-Centric Generation via Long-Horizon Continuation
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.