Rendering quality and performance greatly affect the user's immersion in VR experiences. 3D Gaussian Splatting-based methods can achieve photo-realistic rendering with speeds of over 100 fps in static scenes, but the speed drops below 10 fps in monocular dynamic scenes. Foveated rendering provides a possible solution to accelerate rendering without compromising visual perceptual quality. However, 3DGS and foveated rendering are not compatible. In this paper, we propose Fov-GS, a foveated 3D Gaussian splatting method for rendering dynamic scenes in real time. We introduce a 3D Gaussian forest representation that represents the scene as a forest. To construct the 3D Gaussian forest, we propose a 3D Gaussian forest initialization method based on dynamic-static separation. Subsequently, we propose a 3D Gaussian forest optimization method based on deformation field and Gaussian decomposition to optimize the forest and deformation field. To achieve real-time dynamic scene rendering, we present a 3D Gaussian forest rendering method based on HVS models. Experiments demonstrate that our method not only achieves higher rendering quality in the foveal and salient regions compared to the SOTA methods but also dramatically improves rendering performance, achieving up to 11.33X speedup. We also conducted a user study, and the results prove that the perceptual quality of our method has a high visual similarity with the ground truth.

We introduce the disocclusion headlight, a method for VR selection assistance based on alleviating occlusions at the center of the user's field of view. The user's visualization of the VE is modified to reduce overlap between objects. This way, selection candidate objects have larger image footprints, which facilitates selection. The modification is confined to the center of the frame, with continuity to the periphery of the frame which is rendered conventionally. The selection assistance is provided automatically, without any interaction from the user. Furthermore, our method disoccludes without destroying the local spatial relationships between selection candidates, which allows solving complex selection queries based on the relative position of objects. We have tested our method on three selection tasks, where we compared it to two state-of-the-art VR selection techniques, i.e., the alpha cursor and the flower cone. Our method showed significant advantages in terms of shorter task completion times, and of fewer selection errors.

The demand for fluid simulation and rendering in virtual reality (VR) is increasing. However, achieving high visual quality while maintaining real-time efficiency remains a challenge. Traditional foveated rendering methods balance the simulation quality in the foveated region but neglect the physical realism in the peripheral areas, and fail to account for the perceptual degradation caused by frame rate fluctuations during adaptive updates. To address these challenges, we propose a novel visual perception driven fluid rendering method ViP-Fluid, which further enhances rendering quality while balancing efficiency. Our approach employs a spatiotemporal saliency model for multi-granularity simulation and rendering of Lagrangian fluid systems, and introduces a Perception Threshold for Physical Process Elapsing (PTPE) metric, which guides our temporal acceleration strategy. Through a series of objective experiments, we demonstrate the advantages of our method in rendering quality and performance efficiency. ViP-Fluid demonstrates superior metrics not only in the foveated region but also in the salient and overall regions, achieving up to 2.15 times speed-up compared to the high-resolution Position Based Fluids (PBF) benchmark. Subsequent user experiments further validate the visual perception advantages of ViP-Fluid over both traditional and state-of-the-art methods, confirming the spatiotemporal fidelity of our acceleration strategy as well as a user preference for our approach.

Intuitive motion control is essential for virtual reality, allowing users to manipulate objects naturally while receiving realistic and responsive visual feedback. 3D Gaussian splatting provides real-time, photorealistic scene rendering, making it promising for virtual reality applications. Still, it falls short in accurate motion control of dynamic objects due to its unstructured global motion representation and redundant motion learning. To address these problems, we propose a motion hierarchical Gaussian based dynamic control method. First, a motion hierarchical Gaussian representation is introduced and initialized with semantic and deformation information. Then a motion hierarchical decomposition method is proposed to optimize the local motion in the representation. The representation is next optimized by a local motion analysis based refinement method. We also design a set of motion control operations for the motion hierarchical Gaussian. Experimental results show that our method achieves high-precision motion reconstruction, accurate motion decomposition, real-time, intuitively and immersive VR motion control.