OpenSubject: Leveraging Video-Derived Identity and Diversity Priors for Subject-driven Image Generation and Manipulation

Subject-driven image generation and manipulation are fun-damental for personalized content creation, such as identity-preservingportrait synthesis, multi-character storytelling, and controllable photoediting. However, existing models still struggle with multi-reference set-tings, often showing identity drift and unstable editing quality. A keybottleneck is data: current resources are often limited in scale, diversity,and consistency of subject-level details, especially for unified support ofboth generation and manipulation. To address this gap, we introduceOpenSubject, a video-derived large-scale corpus for subject-driven gen-eration and manipulation. Our pipeline leverages cross-frame identitypriors through four key stages: (i) Video Curation. We apply resolutionand aesthetic filtering to obtain high-quality clips. (ii) Cross-FrameSubject Mining and Pairing. We utilize vision language model (VLM)-based category consensus, local grounding, and diversity-aware pairing toselect image pairs. (iii) Identity-Preserving Reference Image Syn-thesis. We introduce segmentation map-guided outpainting to synthesizeinput images for subject-driven generation and box-guided inpaintingto generate input images for subject-driven manipulation, together withgeometry-aware augmentations and irregular boundary erosion. (iv) Ver-ification and Captioning. We utilize a VLM to validate synthesizedsamples, re-synthesize failed samples based on stage (iii), and then con-struct short and long captions. Furthermore, we introduce a benchmarkcovering both subject-driven generation and subject-driven manipulation,evaluated with a VLM-based judge on identity fidelity, prompt adherence,and consistency. Extensive experiments demonstrate that training onOpenSubject significantly enhances performance in complex scenes.