PhysLayer: Language-Guided Layered Animation with Depth-Aware Physics

TL;DR

PhysLayer is a novel framework that enables language-guided, depth-aware layered animation of static images, improving physical plausibility and control over object dynamics without full 3D reconstruction.

Contribution

It introduces a three-component system combining scene understanding, depth-aware physics simulation, and video synthesis for realistic, controllable image animation.

Findings

CLIP-Similarity increased by 2.2%

FID score improved by 9.3%

Human evaluation showed 24% better physical plausibility

Abstract

Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior approaches have incorporated physics simulators, they remain confined to 2D planar motions and fail to capture depth-aware spatial interactions. We introduce PhysLayer, a novel framework enabling language-guided, depth-aware layered animation of static images. PhysLayer consists of three key components: First, a language-guided scene understanding module that utilizes vision foundation models to decompose scenes into depth-based layers by analyzing object composition, material properties, and physical parameters. Second, a depth-aware layered physics simulation that extends 2D rigid-body dynamics with depth motion and perspective-consistent scaling, enabling more realistic object interactions without requiring full 3D reconstruction. Third, a physics-guided video synthesis module that integrates simulated trajectories with scene-aware relighting for temporally coherent results. Experimental results demonstrate improvements in CLIP-Similarity (+2.2\%), FID score (+9.3\%), and Motion-FID (+3\%), with human evaluation showing enhanced physical plausibility (+24\%) and text-video alignment (+35\%). Our approach provides a practical balance between physical realism and computational efficiency for controllable image animation.