DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos

TL;DR

DreamScene4D introduces a novel method for generating 3D dynamic multi-object scenes from monocular videos by decomposing scenes into background and objects, enabling accurate 4D scene synthesis and object tracking.

Contribution

It is the first approach to generate 3D multi-object scenes from monocular videos using a decomposition-recomposition strategy for scene and motion modeling.

Findings

Effective 4D scene generation on challenging datasets

Accurate 2D persistent point tracking from 3D trajectories

Superior performance in quantitative and user preference evaluations

Abstract

View-predictive generative models provide strong priors for lifting object-centric images and videos into 3D and 4D through rendering and score distillation objectives. A question then remains: what about lifting complete multi-object dynamic scenes? There are two challenges in this direction: First, rendering error gradients are often insufficient to recover fast object motion, and second, view predictive generative models work much better for objects than whole scenes, so, score distillation objectives cannot currently be applied at the scene level directly. We present DreamScene4D, the first approach to generate 3D dynamic scenes of multiple objects from monocular videos via 360-degree novel view synthesis. Our key insight is a "decompose-recompose" approach that factorizes the video scene into the background and object tracks, while also factorizing object motion into 3 components: object-centric deformation, object-to-world-frame transformation, and camera motion. Such decomposition permits rendering error gradients and object view-predictive models to recover object 3D completions and deformations while bounding box tracks guide the large object movements in the scene. We show extensive results on challenging DAVIS, Kubric, and self-captured videos with quantitative comparisons and a user preference study. Besides 4D scene generation, DreamScene4D obtains accurate 2D persistent point track by projecting the inferred 3D trajectories to 2D. We will release our code and hope our work will stimulate more research on fine-grained 4D understanding from videos.