20 citations · 31 across the 8 of their papers we have counts for
8 papers
CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models
Rundi Wu, Ruiqi Gao, Ben Poole +4
We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets…
SimVS: Simulating World Inconsistencies for Robust View Synthesis
Alex Trevithick, Roni Paiss, Philipp Henzler +9
Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illuminat…
CAT3D: Create Anything in 3D with Multi-View Diffusion Models
Ruiqi Gao, Aleksander Holynski, Philipp Henzler +5
Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method f…
EM Distillation for One-step Diffusion Models
Sirui Xie, Zhisheng Xiao, Diederik P Kingma +6
While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, bu…
Video Interpolation with Diffusion Models
Siddhant Jain, Daniel Watson, Eric Tabellion +3
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…
Disentangled 3D Scene Generation with Layout Learning
Dave Epstein, Ben Poole, Ben Mildenhall +2
We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretr…
Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion
Kira Prabhu, Jane Wu, Lynn Tsai +4
This paper presents a novel approach to inpainting 3D regions of a scene, given masked multi-view images, by distilling a 2D diffusion model into a learned 3D scene representation…
ReconFusion: 3D Reconstruction with Diffusion Priors
Rundi Wu, Ben Mildenhall, Philipp Henzler +8
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…