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Phantom: Subject-consistent video generation via cross-modal alignment

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arxiv 2502.11079 v2 pith:RYKAFCUL submitted 2025-02-16 cs.CV cs.AI

Lijie Liu , Tianxiang Ma , Bingchuan Li , Zhuowei Chen , Jiawei Liu , Gen Li , Siyu Zhou , Qian He
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Xinglong Wu
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classification cs.CVcs.AI
keywords generationvideosubject-consistentalignmentcontentcross-modalexistingimage
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The continuous development of foundational models for video generation is evolving into various applications, with subject-consistent video generation still in the exploratory stage. We refer to this as Subject-to-Video, which extracts subject elements from reference images and generates subject-consistent videos following textual instructions. We believe that the essence of subject-to-video lies in balancing the dual-modal prompts of text and image, thereby deeply and simultaneously aligning both text and visual content. To this end, we propose Phantom, a unified video generation framework for both single- and multi-subject references. Building on existing text-to-video and image-to-video architectures, we redesign the joint text-image injection model and drive it to learn cross-modal alignment via text-image-video triplet data. The proposed method achieves high-fidelity subject-consistent video generation while addressing issues of image content leakage and multi-subject confusion. Evaluation results indicate that our method outperforms other state-of-the-art closed-source commercial solutions. In particular, we emphasize subject consistency in human generation, covering existing ID-preserving video generation while offering enhanced advantages.

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Cited by 16 Pith papers

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