Blended-NeRF: Editing NeRF Scenes with Text and Images
In recent years, various disciplines have witnessed revolutionary advancements. From ChatGPT for language models to stable diffusion for generative models and neural radiance fields (NeRF) for computer graphics and vision, groundbreaking techniques have reshaped industries. Among these, NeRF has revolutionized how we represent and render 3D scenes.
NeRF represents a scene as a continuous 3D volume, encoding both geometry and appearance information. Unlike traditional explicit representations, NeRF captures scene properties through a neural network, enabling the synthesis of new views and accurate reconstruction of complex scenes. With its ability to model volumetric density and color, NeRF achieves impressive realism and detail fidelity.
However, despite its potential, NeRF still has limitations. One major challenge is editing NeRF scenes due to their implicit nature and lack of explicit separation between scene components. Unlike methods that provide explicit representations, NeRFs do not clearly differentiate shape, color, and material. Additionally, blending new objects into NeRF scenes requires consistency across multiple views, making editing more complex.
To address these limitations, a new approach called Blended-NeRF was developed. Blended-NeRF allows for ROI-based editing of NeRF scenes guided by text prompts or image patches. It enables editing any part of a scene while preserving the rest without requiring additional feature spaces or masks. The goal is to generate natural-looking and view-consistent results that seamlessly blend with the existing scene.
Blended-NeRF leverages a pre-trained language-image model like CLIP and a NeRF model initialized on an existing scene. The CLIP model guides the generation process based on user-provided text prompts or image patches, enabling the generation of diverse and natural 3D objects. To enable local edits while preserving the scene, a user-friendly GUI localizes a 3D box within the NeRF scene, utilizing depth information for intuitive feedback. For seamless blending, a distance smoothing operation merges the original and synthesized radiance fields along each camera ray.
To improve the quality and coherence of edited NeRF scenes, Blended-NeRF incorporates augmentations and priors suggested in previous works. These include depth regularization, pose sampling, and directional-dependent prompts. By implementing these techniques, Blended-NeRF achieves more realistic and coherent results.
Blended-NeRF opens up new possibilities for editing NeRF scenes, allowing for complex manipulations like object insertion/replacement, object blending, and texture conversion. Its versatility and potential make it a promising tool for various industries.
To learn more about Blended-NeRF and its applications, you can check out the paper and project. Don’t forget to join our ML SubReddit, Discord Channel, and Email Newsletter for the latest AI research news and cool projects. If you have any questions or suggestions, feel free to email us at Asif@marktechpost.com.
About the Author:
Ekrem Çetinkaya is a researcher with a background in deep learning, computer vision, video encoding, and multimedia networking. He received his B.Sc. in 2018 and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. He completed his Ph.D. in 2023 at the University of Klagenfurt, Austria, focusing on video coding enhancements for HTTP adaptive streaming using machine learning.
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