https://3d-diffusion.github.io/

“The core of 3DiM is an image-to-image diffusion model -- 3DiM takes a single reference view and a relative pose as input, and generates a novel view via diffusion. 3DiM can then generate a full 3D consistent scene following our novel stochastic conditioning sampler. The output frames of the scene are generated autoregressively. During the reverse diffusion process of each individual frame, we select a random conditioning frame from the set of previous frames at each denoising step. We demonstrate that stochastic conditioning yields much more 3D consistent results compared to the naïve sampling process which only conditions on a single previous frame.”