DSG-World: Learning a 3D Gaussian World Model from Dual State Videos

Building an efficient and physically consistent world model from limited observations is a long standing challenge in vision and robotics. Many existing world modeling pipelines are based on implicit generative models, which are hard to train and often lack 3D or physical consistency. On the other hand, explicit 3D methods built from a single state often require multi-stage processing-such as segmentation, background completion, and inpainting-due to occlusions. To address this, we leverage two perturbed observations of the same scene under different object configurations. These dual states offer complementary visibility, alleviating occlusion issues during state transitions and enabling more stable and complete reconstruction. In this paper, we present DSG-World, a novel end-to-end framework that explicitly constructs a 3D Gaussian World model from Dual State observations. Our approach builds dual segmentation-aware Gaussian fields and enforces bidirectional photometric and semantic consistency. We further introduce a pseudo intermediate state for symmetric alignment and design collaborative co-pruning trategies to refine geometric completeness. DSG-World enables efficient real-to-simulation transfer purely in the explicit Gaussian representation space, supporting high-fidelity rendering and object-level scene manipulation without relying on dense observations or multi-stage pipelines. Extensive experiments demonstrate strong generalization to novel views and scene states, highlighting the effectiveness of our approach for real-world 3D reconstruction and simulation.

Pointmap Association and Piecewise-Plane Constraint for Consistent and Compact 3D Gaussian Segmentation Field

Achieving a consistent and compact 3D segmentation field is crucial for maintaining semantic coherence across views and accurately representing scene structures. Previous 3D scene segmentation methods rely on video segmentation models to address inconsistencies across views, but the absence of spatial information often leads to object misassociation when object temporarily disappear and reappear. Furthermore, in the process of 3D scene reconstruction, segmentation and optimization are often treated as separate tasks. As a result, optimization typically lacks awareness of semantic category information, which can result in floaters with ambiguous segmentation. To address these challenges, we introduce CCGS, a method designed to achieve both view consistent 2D segmentation and a compact 3D Gaussian segmentation field. CCGS incorporates pointmap association and a piecewise-plane constraint. First, we establish pixel correspondence between adjacent images by minimizing the Euclidean distance between their pointmaps. We then redefine object mask overlap accordingly. The Hungarian algorithm is employed to optimize mask association by minimizing the total matching cost, while allowing for partial matches. To further enhance compactness, the piecewise-plane constraint restricts point displacement within local planes during optimization, thereby preserving structural integrity. Experimental results on ScanNet and Replica datasets demonstrate that CCGS outperforms existing methods in both 2D panoptic segmentation and 3D Gaussian segmentation.