GO-NeRF : Generating Objects in Neural Radiance Fields for Virtual Reality Content Creation
Virtual environments (VEs) are pivotal for virtual, augmented, and mixed reality systems. Despite advances in 3D generation and reconstruction, the direct creation of 3D objects within an established 3D scene (represented as NeRF) for novel VE creation remains a relatively unexplored domain. This pr...
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - 31(2025), 5 vom: 10. Mai, Seite 3087-3097 |
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| Auteur principal: | |
| Autres auteurs: | , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2025
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| Accès à la collection: | IEEE transactions on visualization and computer graphics |
| Sujets: | Journal Article |
| Résumé: | Virtual environments (VEs) are pivotal for virtual, augmented, and mixed reality systems. Despite advances in 3D generation and reconstruction, the direct creation of 3D objects within an established 3D scene (represented as NeRF) for novel VE creation remains a relatively unexplored domain. This process is complex, requiring not only the generation of high-quality 3D objects but also their seamless integration into the existing scene. To this end, we propose a novel pipeline featuring an intuitive interface, dubbed GO-NeRF. Our approach takes text prompts and user-specified regions as inputs and leverages the scene context to generate 3D objects within the scene. We employ a compositional rendering formulation that effectively integrates the generated 3D objects into the scene, utilizing optimized 3D-aware opacity maps to avoid unintended modifications to the original scene. Furthermore, we develop tailored optimization objectives and training strategies to enhance the model's ability to capture scene context and mitigate artifacts, such as floaters, that may occur while optimizing 3D objects within the scene. Extensive experiments conducted on both forward-facing and 360°scenes demonstrate the superior performance of our proposed method in generating objects that harmonize with surrounding scenes and synthesizing high-quality novel view images. The code will be at https://daipengwa.github.io/G0-NeRF/ |
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| Description: | Date Revised 28.04.2025 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2025.3549558 |