Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment
Subject-driven and multi-element video generation are central to controllable video synthesis, but existing methods still struggle to preserve identity consistency and model complex relationships among multiple subjects. In this paper, we propose Aura, a unified framework for high-fidelity and identity-consistent video generation. To better capture scene dynamics and subject interactions, we introduce AI director-level captions that provide dense and structured descriptions of video content. We further leverage a vision-language model (VLM) with learnable queries to extract multimodal semantic features from textual and visual references, covering both global semantics and fine-grained vis...
| Authors | Zixiang Zhou, Zhentao Yu, Yifeng Ma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 July 2026 |
| Last updated at source | 7 July 2026 |
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