title: GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single
    In-the-Wild Image using a Dataset with Levels of Details
summary: Neural implicit functions have brought impressive advances to the
  state-of-the-art of clothed human digitization from multiple or even single
  images. However, despite the progress, current arts still have difficulty
  generalizing to unseen images with complex cloth deformation and body poses. In
  this work, we present GarVerseLOD, a new dataset and framework that paves the
  way to achieving unprecedented robustness in high-fidelity 3D garment
  reconstruction from a single unconstrained image. Inspired by the recent
  success of large generative models, we believe that one key to addressing the
  generalization challenge lies in the quantity and quality of 3D garment data.
  Towards this end, GarVerseLOD collects 6,000 high-quality cloth models with
  fine-grained geometry details manually created by professional artists. In
  addition to the scale of training data, we observe that having disentangled
  granularities of geometry can play an important role in boosting the
  generalization capability and inference accuracy of the learned model. We hence
  craft GarVerseLOD as a hierarchical dataset with levels of details (LOD),
  spanning from detail-free stylized shape to pose-blended garment with
  pixel-aligned details. This allows us to make this highly under-constrained
  problem tractable by factorizing the inference into easier tasks, each narrowed
  down with smaller searching space. To ensure GarVerseLOD can generalize well to
  in-the-wild images, we propose a novel labeling paradigm based on conditional
  diffusion models to generate extensive paired images for each garment model
  with high photorealism. We evaluate our method on a massive amount of
  in-the-wild images. Experimental results demonstrate that GarVerseLOD can
  generate standalone garment pieces with significantly better quality than prior
  approaches. Project page: https://garverselod.github.io/
paper_authors._id: 672b539f70734f59f1e4e657
paper_authors.name: Zhongjin Luo
paper_authors._id: 672b539f70734f59f1e4e658
paper_authors.name: Haolin Liu
paper_authors._id: 672b539f70734f59f1e4e659
paper_authors.name: Chenghong Li
paper_authors._id: 672b539f70734f59f1e4e65a
paper_authors.name: Wanghao Du
paper_authors._id: 672b539f70734f59f1e4e65b
paper_authors.name: Zirong Jin
paper_authors._id: 672b539f70734f59f1e4e65c
paper_authors.name: Wanhu Sun
paper_authors._id: 672b539f70734f59f1e4e65d
paper_authors.name: Yinyu Nie
paper_authors._id: 672b539f70734f59f1e4e65e
paper_authors.name: Weikai Chen
paper_authors._id: 672b539f70734f59f1e4e65f
paper_authors.name: Xiaoguang Han
paper_title: GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single
    In-the-Wild Image using a Dataset with Levels of Details
paper_summary: Neural implicit functions have brought impressive advances to the
  state-of-the-art of clothed human digitization from multiple or even single
  images. However, despite the progress, current arts still have difficulty
  generalizing to unseen images with complex cloth deformation and body poses. In
  this work, we present GarVerseLOD, a new dataset and framework that paves the
  way to achieving unprecedented robustness in high-fidelity 3D garment
  reconstruction from a single unconstrained image. Inspired by the recent
  success of large generative models, we believe that one key to addressing the
  generalization challenge lies in the quantity and quality of 3D garment data.
  Towards this end, GarVerseLOD collects 6,000 high-quality cloth models with
  fine-grained geometry details manually created by professional artists. In
  addition to the scale of training data, we observe that having disentangled
  granularities of geometry can play an important role in boosting the
  generalization capability and inference accuracy of the learned model. We hence
  craft GarVerseLOD as a hierarchical dataset with levels of details (LOD),
  spanning from detail-free stylized shape to pose-blended garment with
  pixel-aligned details. This allows us to make this highly under-constrained
  problem tractable by factorizing the inference into easier tasks, each narrowed
  down with smaller searching space. To ensure GarVerseLOD can generalize well to
  in-the-wild images, we propose a novel labeling paradigm based on conditional
  diffusion models to generate extensive paired images for each garment model
  with high photorealism. We evaluate our method on a massive amount of
  in-the-wild images. Experimental results demonstrate that GarVerseLOD can
  generate standalone garment pieces with significantly better quality than prior
  approaches. Project page: https://garverselod.github.io/
paper_ai_summary: GarVerseLOD, a novel dataset and framework using hierarchical 3D garment models and conditional diffusion models, enhances the robustness and quality of single-image 3D garment reconstruction.
paper_ai_keywords: neural implicit functions clothed human digitization 3D garment reconstruction GarVerseLOD large generative models high-quality cloth models fine-grained geometry details hierarchical dataset levels of details LOD disentangled granularities inference accuracy conditional diffusion models in-the-wild images