Matrix guided method creates editable 4d scenes from one image
MATE4D: Matrix-Guided Editable 4D Generation from a Single Image
Computer Vision and Pattern Recognition
Summary
Creating moving 3D scenes from just one photo is tricky because one picture doesn’t show all angles or movements clearly. The authors developed MATE4D, a method that turns a single image into a 4D (3D plus motion) model that you can change. It uses a matrix of images generated with text instructions to help learn how objects look, move, and appear from different views. Their system keeps the shapes accurate, movements smooth, and backgrounds consistent. This helps make dynamic virtual scenes that can be used in augmented and virtual reality.
What this means in practice
- •For augmented reality developers: Generate editable and temporally stable 4D scenes from a single image to improve AR content creation workflows with consistent backgrounds and smooth motion.
- •For virtual reality content creators: Create dynamic 3D scenes that can be controlled and animated from one input image, enhancing VR world-building with fewer input data requirements.
- •For video game artists: Produce realistic, editable 4D assets from a single concept image to speed up asset creation in game development pipelines.$Commercial implications: Allows game studios to sell tools that convert images into dynamic 3D models with animation control, reducing artist workload.
Authors
Xiaotian Chen, Dongfu Yin
Abstract
Generative models have rapidly pushed content creation be-yond 2D imagery toward dynamic 3D and 4D scene synthesis. Yet pro-ducing realistic and temporally stable 4D content from a single image is still difficult because one view provides limited structural cues and weak motion evidence. We introduce MATE4D, a framework that converts one input image into editable dynamic 4D content. Our method constructs a spatio-temporal multi-view image matrix with text-guided background manipulation, delivering coherent supervision over viewpoint, appear-ance, and motion. These synthesized observations are used to optimize 3D Gaussian primitives, which are then animated through a lightweight deformation module to form a 4D representation. The resulting scenes preserve geometry more faithfully, maintain smoother temporal behavior, and keep background edits more consistent, reducing context ambiguity and motion artifacts. Experiments on Objaverse-XL and Diffusion4D show that MATE4D outperforms strong baselines in visual quality, effi-ciency, and controllability, supporting practical AR/VR content creation.