PairHuman: A High-Fidelity Photographic Dataset for Customized Dual-Person Generation

• URL: http://arxiv.org/abs/2511.16712v2
• Date: Mon, 24 Nov 2025 07:58:20 GMT
• Title: PairHuman: A High-Fidelity Photographic Dataset for Customized Dual-Person Generation
• Abstract summary: PairHuman is the first large-scale benchmark dataset specifically designed for generating dual-person portraits that meet high photographic standards.<n>The dataset contains more than 100K images that capture a variety of scenes, attire, and dual-person interactions.<n>Our dataset and method produce highly customized portraits with superior visual quality that are tailored to human preferences.
• Score: 21.29027253118143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized dual-person portrait customization has considerable potential applications, such as preserving emotional memories and facilitating wedding photography planning. However, the absence of a benchmark dataset hinders the pursuit of high-quality customization in dual-person portrait generation. In this paper, we propose the PairHuman dataset, which is the first large-scale benchmark dataset specifically designed for generating dual-person portraits that meet high photographic standards. The PairHuman dataset contains more than 100K images that capture a variety of scenes, attire, and dual-person interactions, along with rich metadata, including detailed image descriptions, person localization, human keypoints, and attribute tags. We also introduce DHumanDiff, which is a baseline specifically crafted for dual-person portrait generation that features enhanced facial consistency and simultaneously balances in personalized person generation and semantic-driven scene creation. Finally, the experimental results demonstrate that our dataset and method produce highly customized portraits with superior visual quality that are tailored to human preferences. Our dataset is publicly available at https://github.com/annaoooo/PairHuman.

Related papers

• Ar2Can: An Architect and an Artist Leveraging a Canvas for Multi-Human Generation [66.43387826787243]
  Ar2Can is a novel framework that disentangles spatial planning from identity rendering for multi-human generation.<n>Architect module predicts structured layouts, specifying where each person should appear.<n>Artist module synthesizes images, guided by a spatially-grounded face matching reward.
  arXiv  Detail & Related papers  (2025-11-27T18:45:23Z)
• IC-Portrait: In-Context Matching for View-Consistent Personalized Portrait [51.18967854258571]
  IC-Portrait is a novel framework designed to accurately encode individual identities for personalized portrait generation.<n>Our key insight is that pre-trained diffusion models are fast learners for in-context dense correspondence matching.<n>We show that IC-Portrait consistently outperforms existing state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2025-01-28T18:59:03Z)
• Personalized Representation from Personalized Generation [36.848215621708235]
  We formalize the challenge of using personalized synthetic data to learn personalized representations.<n>We show that our method improves personalized representation learning for diverse downstream tasks.
  arXiv  Detail & Related papers  (2024-12-20T18:59:03Z)
• Controllable Human Image Generation with Personalized Multi-Garments [46.042383679103125]
  BootComp is a novel framework based on text-to-image diffusion models for controllable human image generation with multiple reference garments.<n>We propose a data generation pipeline to construct a large synthetic dataset, consisting of human and multiple-garment pairs.<n>We show the wide-applicability of our framework by adapting it to different types of reference-based generation in the fashion domain.
  arXiv  Detail & Related papers  (2024-11-25T12:37:13Z)
• Diffusion Models are Efficient Data Generators for Human Mesh Recovery [55.37787289869703]
  We show that synthetic data created by generative models is complementary to CG-rendered data.<n>We propose an effective data generation pipeline based on recent diffusion models, termed HumanWild.<n>Our work could pave the way for scaling up 3D human recovery to in-the-wild scenes.
  arXiv  Detail & Related papers  (2024-03-17T06:31:16Z)
• CapHuman: Capture Your Moments in Parallel Universes [60.06408546134581]
  We present a new framework named CapHuman. CapHuman encodes identity features and then learns to align them into the latent space. We introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner.
  arXiv  Detail & Related papers  (2024-02-01T14:41:59Z)
• Portrait Interpretation and a Benchmark [49.484161789329804]
  The proposed portrait interpretation recognizes the perception of humans from a new systematic perspective. We construct a new dataset that contains 250,000 images labeled with identity, gender, age, physique, height, expression, and posture of the whole body and arms. Our experimental results demonstrate that combining the tasks related to portrait interpretation can yield benefits.
  arXiv  Detail & Related papers  (2022-07-27T06:25:09Z)
• StyleGAN-Human: A Data-Centric Odyssey of Human Generation [96.7080874757475]
  This work takes a data-centric perspective and investigates multiple critical aspects in "data engineering" We collect and annotate a large-scale human image dataset with over 230K samples capturing diverse poses and textures. We rigorously investigate three essential factors in data engineering for StyleGAN-based human generation, namely data size, data distribution, and data alignment.
  arXiv  Detail & Related papers  (2022-04-25T17:55:08Z)
• MyStyle: A Personalized Generative Prior [38.3436972491162]
  We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person.
  arXiv  Detail & Related papers  (2022-03-31T17:59:19Z)

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.