Accurate 4D reconstruction from limited monocular observations

Establish an algorithmic framework that transforms limited monocular observations—specifically single-camera videos or sparse single-view images—into an accurate model of the dynamically changing 3D world (a 4D scene), overcoming the fundamental ambiguity and incompleteness inherent in single-view capture to enable reliable dynamic scene reconstruction and rendering.

Background

The paper motivates the problem by noting that real-world capture typically provides only a partial snapshot of a dynamic 3D environment, which makes recovering accurate 4D structure and motion from monocular input inherently challenging. While multi-view static datasets and synchronized multi-view dynamic datasets can enable reconstruction, such data are difficult to obtain at scale for dynamic scenes. The authors aim to address this challenge by training a multi-view video diffusion model and proposing sampling and reconstruction strategies that work from monocular videos.

This statement frames the broader research gap: despite progress in 3D/4D methods, reliably converting monocular inputs into accurate dynamic 3D models remains unresolved and has substantial practical importance for applications such as robotics, film-making, video games, and augmented reality.