CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-centric 3D Scene Generation
arXiv:2607.03819v1 Announce Type: cross Abstract: Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interpretation. These factors hinder the creation of viewpoint-consistent and semantically aligned visual content, as well as the construction of accurate geometric structures. In this paper, we propose CGGS, a text-to-3D framework aiming to enhance 3D-content-awareness and address geometric distortions in ego-centric scene generation. Firstly, the Ego-centric Generator is proposed by fine-tuning a Mult
The continuous advancements in AI and 3D reconstruction technologies are converging, making sophisticated ego-centric scene generation more feasible and necessary for virtual and augmented reality applications.
Improving the accuracy and consistency of ego-centric 3D scene generation is crucial for developing robust AI systems that interact with complex real-world environments, influencing sectors from robotics to gaming.
This research introduces a framework that directly addresses key limitations in generating consistent and geometrically accurate 3D scenes from a first-person perspective, promising more realistic and usable digital environments.
- · AI developers
- · Metaverse platforms
- · Robotics companies
- · Gaming industry
- · Companies relying on less sophisticated 3D reconstruction methods
Generative AI models will produce more coherent and physically plausible 3D environments for training and simulation.
The fidelity of virtual and augmented reality experiences will drastically increase, leading to broader adoption and new applications.
Advanced ego-centric 3D understanding could enable AI agents to perform complex physical tasks in unstructured environments with greater autonomy and precision.
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Read at arXiv cs.AI