title: Pippo: High-Resolution Multi-View Humans from a Single Image
summary: We present Pippo, a generative model capable of producing 1K resolution dense
  turnaround videos of a person from a single casually clicked photo. Pippo is a
  multi-view diffusion transformer and does not require any additional inputs -
  e.g., a fitted parametric model or camera parameters of the input image. We
  pre-train Pippo on 3B human images without captions, and conduct multi-view
  mid-training and post-training on studio captured humans. During mid-training,
  to quickly absorb the studio dataset, we denoise several (up to 48) views at
  low-resolution, and encode target cameras coarsely using a shallow MLP. During
  post-training, we denoise fewer views at high-resolution and use pixel-aligned
  controls (e.g., Spatial anchor and Plucker rays) to enable 3D consistent
  generations. At inference, we propose an attention biasing technique that
  allows Pippo to simultaneously generate greater than 5 times as many views as
  seen during training. Finally, we also introduce an improved metric to evaluate
  3D consistency of multi-view generations, and show that Pippo outperforms
  existing works on multi-view human generation from a single image.
paper_authors._id: 67aceb3315317375eccddc3b
paper_authors.name: Yash Kant
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:24:09.195Z
paper_authors._id: 638546d0a179f856005ae310
paper_authors.avatarUrl: /avatars/f7f2fface336c8e168a1daaf9fd4d40c.svg
paper_authors.fullname: yashkant
paper_authors.type: user
paper_authors.user: yashkant
paper_authors._id: 67aceb3315317375eccddc3c
paper_authors.name: Ethan Weber
paper_authors._id: 67aceb3315317375eccddc3d
paper_authors.name: Jin Kyu Kim
paper_authors._id: 67aceb3315317375eccddc3e
paper_authors.name: Rawal Khirodkar
paper_authors._id: 67aceb3315317375eccddc3f
paper_authors.name: Su Zhaoen
paper_authors._id: 67aceb3315317375eccddc40
paper_authors.name: Julieta Martinez
paper_authors._id: 67aceb3315317375eccddc41
paper_authors.name: Igor Gilitschenski
paper_authors._id: 67aceb3315317375eccddc42
paper_authors.name: Shunsuke Saito
paper_authors._id: 67aceb3315317375eccddc43
paper_authors.name: Timur Bagautdinov
paper_title: Pippo: High-Resolution Multi-View Humans from a Single Image
paper_summary: We present Pippo, a generative model capable of producing 1K resolution dense
  turnaround videos of a person from a single casually clicked photo. Pippo is a
  multi-view diffusion transformer and does not require any additional inputs -
  e.g., a fitted parametric model or camera parameters of the input image. We
  pre-train Pippo on 3B human images without captions, and conduct multi-view
  mid-training and post-training on studio captured humans. During mid-training,
  to quickly absorb the studio dataset, we denoise several (up to 48) views at
  low-resolution, and encode target cameras coarsely using a shallow MLP. During
  post-training, we denoise fewer views at high-resolution and use pixel-aligned
  controls (e.g., Spatial anchor and Plucker rays) to enable 3D consistent
  generations. At inference, we propose an attention biasing technique that
  allows Pippo to simultaneously generate greater than 5 times as many views as
  seen during training. Finally, we also introduce an improved metric to evaluate
  3D consistency of multi-view generations, and show that Pippo outperforms
  existing works on multi-view human generation from a single image.
paper_ai_keywords: generative model diffusion transformer multi-view prompt pre-train human images mid-training post-training studio captured humans denoising views low-resolution shallow MLP high-resolution pixel-aligned controls Spatial anchor Plucker rays 3D consistent generations inference attention biasing technique improved metric 3D consistency multi-view human generation