Despite recent advances in Gaussian Splatting techniques-such as Superpoint Gaussian Splatting (SP-GS), which enables real-time, high-fidelity rendering-3D reconstruction of dynamic scenes remains a significant challenge in computer vision. However, SP-GS requires nearly an hour for dynamic scene optimization, severely limiting its practical applications in AR and VR. To address this limitation, we propose Fast SP-GS, an efficient approach that reduces training time to mere minutes. Building upon acceleration methods for static scenes (e.g., Mini-Splatting, Taming 3DGS, FlashGS), our novel 2D-GS-based framework enhances speed and quality via three key innovations: First, an aggressive 2D-GS densification strategy reduces required training iterations, while a Gaussian simplification strategy minimizes redundant parameters. Second, a novel 2D-GS optical flow loss provides explicit motion supervision, accelerating convergence. Third, an optimized CUDA implementation maximizes rendering efficiency. Extensive experiments on synthetic and real-world datasets confirm that Fast SP-GS reconstructs dynamic scenes in minutes, surpassing SP-GS in both rendering quality and computational efficiency. The source code is available at https://github.com/dnvtmf/Fast-SP-GS.

Generating articulated objects, such as laptops and microwaves, is a crucial yet challenging task with extensive applications in Embodied AI and AR/VR. Current image-to-3D methods primarily focus on surface geometry and texture, neglecting part decomposition and articulation modeling. Meanwhile, neural reconstruction approaches (e.g., NeRF or Gaussian Splatting) rely on dense multi-view or interaction data, limiting their scalability. In this paper, we introduce DreamArt, a novel framework for generating high-fidelity, interactable articulated assets from single-view images. DreamArt employs a three-stage pipeline: firstly, it reconstructs part‑segmented and complete 3D object meshes through a combination of image-to-3D generation, mask-prompted 3D segmentation, and part amodal completion. Second, we fine-tune a video diffusion model to capture part-level articulation priors, leveraging movable part masks as prompt and amodal images to mitigate ambiguities caused by occlusion. Finally, DreamArt optimizes the articulation motion, represented by a dual quaternion, and conducts global texture refinement and repainting to ensure coherent, high-quality textures across all parts. Experimental results demonstrate that DreamArt effectively generates high-quality articulated objects, possessing accurate part shape, high appearance fidelity, and plausible articulation, thereby providing a scalable solution for articulated asset generation.

While novel view synthesis for dynamic scenes has made significant progress, capturing skeleton models of objects and re-posing them remains a challenging task. To tackle this problem, in this paper, we propose a novel approach to automatically discover the associated skeleton model for dynamic objects from videos without the need for object-specific templates. Our approach utilizes 3D Gaussian Splatting and superpoints to reconstruct dynamic objects. Treating superpoints as rigid parts, we can discover the underlying skeleton model through intuitive cues and optimize it using the kinematic model. Besides, an adaptive control strategy is applied to avoid the emergence of redundant superpoints. Extensive experiments demonstrate the effectiveness and efficiency of our method in obtaining re-posable 3D objects. Not only can our approach achieve excellent visual fidelity, but it also allows for the real-time rendering of high-resolution images.