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.
@article{arxiv.2502.07785,
title = {Pippo: High-Resolution Multi-View Humans from a Single Image},
author = {Yash Kant and Ethan Weber and Jin Kyu Kim and Rawal Khirodkar and Su Zhaoen and Julieta Martinez and Igor Gilitschenski and Shunsuke Saito and Timur Bagautdinov},
journal= {arXiv preprint arXiv:2502.07785},
year = {2025}
}