Vera: Identity-Faithful Human Subject-to-Video Generation
Subject-to-video (S2V) generation has made substantial progress in preserving reference subjects across diverse categories, yet generic subject consistency remains insufficient for human-centric generation. A video may appear globally consistent while identity-critical human details still drift across frames, poses, and interactions. This issue becomes more severe in multi-person scenarios, where incorrect identity-role binding leads to subject confusion, attribute swapping, and excessive copying of reference-specific appearance cues. We propose Vera, a unified human-centric S2V framework for
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “Vera: Identity-Faithful Human Subject-to-Video Generation” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Yulong Xu →
“Vera: Identity-Faithful Human Subject-to-Video Generation”
- LinkedLinked via arxiv author · 85%Xinyue Liu →
“Vera: Identity-Faithful Human Subject-to-Video Generation”
- LinkedLinked via arxiv author · 85%Shujuan Li →
“Vera: Identity-Faithful Human Subject-to-Video Generation”