Motion Decoupled 3D Gaussian Splatting for Dynamic Object Representation
Xiao Hu, Libo Long, Jochen Lang
Abstract
Dynamic object modeling is a critical challenge in 3D scene reconstruction. Previous methods typically maintain a canonical space to represent the object model, and a deformation field to express the object motion. However, this approach fails when the object undergoes large motions. The position variation caused by significant motion not only complicates the establishment of a canonical space, but also misleads the interpretation of the deformation field. To overcome the above weaknesses, we propose Motion Decoupled Dynamic 3D Gaussian Splatting (M5D-GS), the first 3D-GS model that separates motion and deformation modeling for dynamic object representation with large motion from a monocular camera. M5D-GS increases the practicality of 3D-GS, as it is common for objects to move, rotate, and deform simultaneously. Current datasets only contain object deformations with slight motions. We introduce a pipeline to reuse current benchmarks by adding large motions into the scene. We also introduce a new benchmark featuring several new scenes with complex motions, scenes augmented from previous datasets, and some real world recorded testcases, to fully demonstrate our improvements. Our M5D-GS significantly increases the accuracy under large motion scenarios while maintaining high rendering speed, which makes it suitable for dynamic object representation tasks including 4D novel view synthesis and real-time rendering.
BibTeX
@article{Hu_Long_Lang_2025, title={Motion Decoupled 3D Gaussian Splatting for Dynamic Object Representation}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32373}, DOI={10.1609/aaai.v39i4.32373}, abstractNote={Dynamic object modeling is a critical challenge in 3D scene reconstruction. Previous methods typically maintain a canonical space to represent the object model, and a deformation field to express the object motion. However, this approach fails when the object undergoes large motions. The position variation caused by significant motion not only complicates the establishment of a canonical space, but also misleads the interpretation of the deformation field. To overcome the above weaknesses, we propose Motion Decoupled Dynamic 3D Gaussian Splatting (M5D-GS), the first 3D-GS model that separates motion and deformation modeling for dynamic object representation with large motion from a monocular camera. M5D-GS increases the practicality of 3D-GS, as it is common for objects to move, rotate, and deform simultaneously. Current datasets only contain object deformations with slight motions. We introduce a pipeline to reuse current benchmarks by adding large motions into the scene. We also introduce a new benchmark featuring several new scenes with complex motions, scenes augmented from previous datasets, and some real world recorded testcases, to fully demonstrate our improvements. Our M5D-GS significantly increases the accuracy under large motion scenarios while maintaining high rendering speed, which makes it suitable for dynamic object representation tasks including 4D novel view synthesis and real-time rendering.}, number={4}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Hu, Xiao and Long, Libo and Lang, Jochen}, year={2025}, month={Apr.}, pages={3590-3598} }