ID-Composer: Multi-Subject Video Synthesis with Hierarchical Identity Preservation

• URL: http://arxiv.org/abs/2511.00511v2
• Date: Tue, 04 Nov 2025 03:11:03 GMT
• Title: ID-Composer: Multi-Subject Video Synthesis with Hierarchical Identity Preservation
• Authors: Panwang Pan, Jingjing Zhao, Yuchen Lin, Chenguo Lin, Chenxin Li, Haopeng Li, Honglei Yan, Tingting Shen, Yadong Mu,
• Abstract summary: Video generative models pretrained on large-scale datasets can produce high-quality videos, but are often conditioned on text or a single image.<n>We introduce ID-Composer, a novel framework that tackles multi-subject video generation from a text prompt and reference images.
• Score: 48.59900036213667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video generative models pretrained on large-scale datasets can produce high-quality videos, but are often conditioned on text or a single image, limiting controllability and applicability. We introduce ID-Composer, a novel framework that addresses this gap by tackling multi-subject video generation from a text prompt and reference images. This task is challenging as it requires preserving subject identities, integrating semantics across subjects and modalities, and maintaining temporal consistency. To faithfully preserve the subject consistency and textual information in synthesized videos, ID-Composer designs a hierarchical identity-preserving attention mechanism, which effectively aggregates features within and across subjects and modalities. To effectively allow for the semantic following of user intention, we introduce semantic understanding via pretrained vision-language model (VLM), leveraging VLM's superior semantic understanding to provide fine-grained guidance and capture complex interactions between multiple subjects. Considering that standard diffusion loss often fails in aligning the critical concepts like subject ID, we employ an online reinforcement learning phase to drive the overall training objective of ID-Composer into RLVR. Extensive experiments demonstrate that our model surpasses existing methods in identity preservation, temporal consistency, and video quality.

Related papers

• Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation [22.845591588026366]
  We propose a framework that provides explicit, structured supervision from high-level concepts to fine-grained appearances.<n>At the conceptual level, we introduce a VAE dropout training strategy that randomly omits reference VAE features.<n>At the appearance level, we integrate the VLM-derived correspondences into a correspondence-aware masked attention module.
  arXiv  Detail & Related papers  (2026-02-03T12:13:29Z)
• DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer [21.788582116033684]
  Video Face Swapping (VFS) requires seamlessly injecting a source identity into a target video.<n>Existing methods struggle to maintain identity similarity and attribute preservation while preserving temporal consistency.<n>We propose a comprehensive framework to seamlessly transfer the superiority of Image Face Swapping to the video domain.
  arXiv  Detail & Related papers  (2026-01-04T08:07:11Z)
• AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation [58.844504598618094]
  We propose AlcheMinT, a unified framework that introduces explicit timestamps conditioning for subject-driven video generation.<n>Our approach introduces a novel positional encoding mechanism that unlocks the encoding of temporal intervals, associated in our case with subject identities.<n>We incorporate subject-descriptive text tokens to strengthen binding between visual identity and video captions, mitigating ambiguity during generation.
  arXiv  Detail & Related papers  (2025-12-11T18:59:34Z)
• BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration [56.98981194478512]
  We propose a unified framework that handles a broad range of subject-to-video scenarios.<n>We introduce an MLLM-DiT framework in which a pretrained multimodal large language model performs deep cross-modal reasoning to ground entities.<n>Experiments on the OpenS2V benchmark demonstrate that our method achieves superior performance across subject consistency, naturalness, and text relevance in generated videos.
  arXiv  Detail & Related papers  (2025-10-01T02:41:11Z)
• Compositional Video Synthesis by Temporal Object-Centric Learning [3.2228025627337864]
  We present a novel framework for compositional video synthesis that leverages temporally consistent object-centric representations.<n>Our approach explicitly captures temporal dynamics by learning pose invariant object-centric slots and conditioning them on pretrained diffusion models.<n>This design enables high-quality, pixel-level video synthesis with superior temporal coherence.
  arXiv  Detail & Related papers  (2025-07-28T14:11:04Z)
• SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction [65.15449703659772]
  Video Object (VOS) is a core task in computer vision, requiring models to track and segment target objects across video frames.<n>We propose Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations.<n>SeC achieves an 11.8-point improvement over SAM SeCVOS, establishing a new state-of-the-art concept-aware video object segmentation.
  arXiv  Detail & Related papers  (2025-07-21T17:59:02Z)
• PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement [26.89021788485701]
  PolyVivid is a multi-subject video customization framework that enables flexible and identity-consistent generation.<n>In experiments, PolyVivid achieves superior performance in identity fidelity, video realism, and subject alignment, outperforming existing open-source and commercial baselines.
  arXiv  Detail & Related papers  (2025-06-09T15:11:09Z)
• CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance [34.345125922868]
  We propose CINEMA, a novel framework for coherent multi-subject video generation by leveraging Multimodal Large Language Model (MLLM)<n>Our approach eliminates the need for explicit correspondences between subject images and text entities, mitigating ambiguity and reducing annotation effort.<n>Our framework can be conditioned on varying numbers of subjects, offering greater flexibility in personalized content creation.
  arXiv  Detail & Related papers  (2025-03-13T14:07:58Z)
• Hierarchical Banzhaf Interaction for General Video-Language Representation Learning [60.44337740854767]
  Multimodal representation learning plays an important role in the artificial intelligence domain.<n>We introduce a new approach that models video-text as game players using multivariate cooperative game theory.<n>We extend our original structure into a flexible encoder-decoder framework, enabling the model to adapt to various downstream tasks.
  arXiv  Detail & Related papers  (2024-12-30T14:09:15Z)
• Temporal Contrastive Learning for Video Temporal Reasoning in Large Vision-Language Models [44.99833362998488]
  Temporal Semantic Alignment via Dynamic Prompting (TSADP) is a novel framework that enhances temporal reasoning capabilities.<n>We evaluate TSADP on the VidSitu dataset, augmented with enriched temporal annotations.<n>Our analysis highlights the robustness, efficiency, and practical utility of TSADP, making it a step forward in the field of video-language understanding.
  arXiv  Detail & Related papers  (2024-12-16T02:37:58Z)
• Bridging Vision and Language: Modeling Causality and Temporality in Video Narratives [0.0]
  We propose an enhanced framework that integrates a Causal-Temporal Reasoning Module into state-of-the-art LVLMs.<n>CTRM comprises two key components: the Causal Dynamics (CDE) and the Temporal Learner (TRL)<n>We design a multi-stage learning strategy to optimize the model, combining pre-training on large-scale video-text datasets.
  arXiv  Detail & Related papers  (2024-12-14T07:28:38Z)
• MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing [90.06041718086317]
  We propose a unified Multi-alignment Diffusion, dubbed as MagDiff, for both tasks of high-fidelity video generation and editing. The proposed MagDiff introduces three types of alignments, including subject-driven alignment, adaptive prompts alignment, and high-fidelity alignment.
  arXiv  Detail & Related papers  (2023-11-29T03:36:07Z)
• Contrastive Transformation for Self-supervised Correspondence Learning [120.62547360463923]
  We study the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. Our framework outperforms the recent self-supervised correspondence methods on a range of visual tasks.
  arXiv  Detail & Related papers  (2020-12-09T14:05:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.