PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal
  Interaction and Enhancement
Despite recent advances in video generation, existing models still lack
fine-grained controllability, especially for multi-subject customization with
consistent identity and interaction. In this paper, we propose PolyVivid, a
multi-subject video customization framework that enables flexible and
identity-consistent generation. To establish accurate correspondences between
subject images and textual entities, we design a VLLM-based text-image fusion
module that embeds visual identities into the textual space for precise
grounding. To further enhance identity preservation and subject interaction, we
propose a 3D-RoPE-based enhancement module that enables structured
bidirectional fusion between text and image embeddings. Moreover, we develop an
attention-inherited identity injection module to effectively inject fused
identity features into the video generation process, mitigating identity drift.
Finally, we construct an MLLM-based data pipeline that combines MLLM-based
grounding, segmentation, and a clique-based subject consolidation strategy to
produce high-quality multi-subject data, effectively enhancing subject
distinction and reducing ambiguity in downstream video generation. Extensive
experiments demonstrate that PolyVivid achieves superior performance in
identity fidelity, video realism, and subject alignment, outperforming existing
open-source and commercial baselines.
6847de223ec10bdd8ab4e02a
Teng Hu
6847de223ec10bdd8ab4e02b
Zhentao Yu
6847de223ec10bdd8ab4e02c
Zhengguang Zhou
6847de223ec10bdd8ab4e02d
Jiangning Zhang
6847de223ec10bdd8ab4e02e
Yuan Zhou
6847de223ec10bdd8ab4e02f
Qinglin Lu
6847de223ec10bdd8ab4e030
Ran Yi
PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal
  Interaction and Enhancement
Despite recent advances in video generation, existing models still lack
fine-grained controllability, especially for multi-subject customization with
consistent identity and interaction. In this paper, we propose PolyVivid, a
multi-subject video customization framework that enables flexible and
identity-consistent generation. To establish accurate correspondences between
subject images and textual entities, we design a VLLM-based text-image fusion
module that embeds visual identities into the textual space for precise
grounding. To further enhance identity preservation and subject interaction, we
propose a 3D-RoPE-based enhancement module that enables structured
bidirectional fusion between text and image embeddings. Moreover, we develop an
attention-inherited identity injection module to effectively inject fused
identity features into the video generation process, mitigating identity drift.
Finally, we construct an MLLM-based data pipeline that combines MLLM-based
grounding, segmentation, and a clique-based subject consolidation strategy to
produce high-quality multi-subject data, effectively enhancing subject
distinction and reducing ambiguity in downstream video generation. Extensive
experiments demonstrate that PolyVivid achieves superior performance in
identity fidelity, video realism, and subject alignment, outperforming existing
open-source and commercial baselines.
PolyVivid is a multi-subject video customization framework that uses text-image fusion, 3D-RoPE enhancement, attention-inherited identity injection, and MLLM-based data processing to ensure identity consistency and realistic video generation.
VLLM-based text-image fusion 3D-RoPE-based enhancement attention-inherited identity injection MLLM-based data pipeline identity fidelity video realism subject alignment