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UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization

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arxiv 2408.05939 v2 pith:ASJETGDD submitted 2024-08-12 cs.CV

Junjie He , Yifeng Geng , Liefeng Bo This is my paper
classification cs.CV
keywords moduleuniportraitcustomizationimagesingle-embeddingexistingfacial
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents UniPortrait, an innovative human image personalization framework that unifies single- and multi-ID customization with high face fidelity, extensive facial editability, free-form input description, and diverse layout generation. UniPortrait consists of only two plug-and-play modules: an ID embedding module and an ID routing module. The ID embedding module extracts versatile editable facial features with a decoupling strategy for each ID and embeds them into the context space of diffusion models. The ID routing module then combines and distributes these embeddings adaptively to their respective regions within the synthesized image, achieving the customization of single and multiple IDs. With a carefully designed two-stage training scheme, UniPortrait achieves superior performance in both single- and multi-ID customization. Quantitative and qualitative experiments demonstrate the advantages of our method over existing approaches as well as its good scalability, e.g., the universal compatibility with existing generative control tools. The project page is at https://aigcdesigngroup.github.io/UniPortrait-Page/ .

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GroupVideo: Multi-Identity Customized Text-to-Video Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    GroupVideo generates multi-person videos from reference photos plus text, using multimodal identity alignment and ID localization to keep each person's identity consistent.

  2. MultiAnimate: A Unified Framework for Controllable Multi-Character Animation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A diffusion-based framework that animates multiple characters in one scene from separate reference images and pose sequences while preserving each character's identity.

  3. Human Preference-Aligned Concept Customization Benchmark via Decomposed Evaluation

    cs.CV 2025-09 conditional novelty 6.0 of 10

    D-GPTScore, which averages GPT-4o's per-aspect ratings of concept-customized images, correlates with human preference at 0.78 Pearson on the new CC-AlignBench, beating prior metrics.

  4. OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A benchmark and five-million-clip dataset for evaluating and training subject-to-video generation models, with three new metrics for subject consistency, naturalness, and text alignment.

  5. Retrieval Augmented Comic Image Generation

    cs.CV 2025-06 reject novelty 5.0 of 10

    RaCig combines retrieval-based character assignment with regional IP-Adapter and ControlNet injection to generate comic panels with consistent characters and diverse gestures.

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