Pippo: High-Resolution Multi-View Humans from a Single Image
Authors: name: Yash Kant, name: Ethan Weber, name: Jin Kyu Kim, name: Rawal Khirodkar, name: Su Zhaoen, name: Julieta Martinez, name: Igor Gilitschenski, name: Shunsuke Saito, name: Timur Bagautdinov

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.