VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video
  Diffusion Models
Authors: name: Geonung Kim, name: Janghyeok Han, name: Sunghyun Cho

In this paper, we propose VideoFrom3D, a novel framework for synthesizing
high-quality 3D scene videos from coarse geometry, a camera trajectory, and a
reference image. Our approach streamlines the 3D graphic design workflow,
enabling flexible design exploration and rapid production of deliverables. A
straightforward approach to synthesizing a video from coarse geometry might
condition a video diffusion model on geometric structure. However, existing
video diffusion models struggle to generate high-fidelity results for complex
scenes due to the difficulty of jointly modeling visual quality, motion, and
temporal consistency. To address this, we propose a generative framework that
leverages the complementary strengths of image and video diffusion models.
Specifically, our framework consists of a Sparse Anchor-view Generation (SAG)
and a Geometry-guided Generative Inbetweening (GGI) module. The SAG module
generates high-quality, cross-view consistent anchor views using an image
diffusion model, aided by Sparse Appearance-guided Sampling. Building on these
anchor views, GGI module faithfully interpolates intermediate frames using a
video diffusion model, enhanced by flow-based camera control and structural
guidance. Notably, both modules operate without any paired dataset of 3D scene
models and natural images, which is extremely difficult to obtain.
Comprehensive experiments show that our method produces high-quality,
style-consistent scene videos under diverse and challenging scenarios,
outperforming simple and extended baselines.