Human4DiT: Free-view Human Video Generation with 4D Diffusion
  Transformer
Authors: name: Ruizhi Shao, name: Youxin Pang, name: Zerong Zheng, name: Jingxiang Sun, name: Yebin Liu

We present a novel approach for generating high-quality, spatio-temporally
coherent human videos from a single image under arbitrary viewpoints. Our
framework combines the strengths of U-Nets for accurate condition injection and
diffusion transformers for capturing global correlations across viewpoints and
time. The core is a cascaded 4D transformer architecture that factorizes
attention across views, time, and spatial dimensions, enabling efficient
modeling of the 4D space. Precise conditioning is achieved by injecting human
identity, camera parameters, and temporal signals into the respective
transformers. To train this model, we curate a multi-dimensional dataset
spanning images, videos, multi-view data and 3D/4D scans, along with a
multi-dimensional training strategy. Our approach overcomes the limitations of
previous methods based on GAN or UNet-based diffusion models, which struggle
with complex motions and viewpoint changes. Through extensive experiments, we
demonstrate our method's ability to synthesize realistic, coherent and
free-view human videos, paving the way for advanced multimedia applications in
areas such as virtual reality and animation. Our project website is
https://human4dit.github.io.