5 accepted papers
We present 4DiM, a cascaded diffusion model for 4D novel view synthesis (NVS), supporting generation with arbitrary camera trajectories and timestamps, in natural scenes, conditioned on one or more images. With a novel architecture and sampling procedure, we enable training on a mixture of 3D (with…
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However recovering a high-quality NeRF typically requires tens to hundreds of input images resulting in a time-consuming capture process. We present ReconFusion to recons…
We present VIDIM a generative model for video interpolation which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data VIDIM uses cascaded diffusion models to first generate the target video at low resolution and then gener…
We present 3DiM (pronounced "three-dim"), a diffusion model for 3D novel view synthesis from as few as a single image. The core of 3DiM is an image-to-image diffusion model -- 3DiM takes a single reference view and their poses as inputs, and generates a novel view via diffusion. 3DiM can then genera…
Diffusion models have emerged as an expressive family of generative models rivaling GANs in sample quality and autoregressive models in likelihood scores. Standard diffusion models typically require hundreds of forward passes through the model to generate a single high-fidelity sample. We introduce…