Computer graphics and vision have witnessed a paradigm shift in the way 3D scenes are represented and rendered with the emergence of neural radiance fields (NeRF). As Marktechpost explains, NeRF represents a scene as a continuous 3D volume, encoding geometry and appearance information.
Blended-NeRF is a new AI model that enables 3D object generation with unprecedented realism and flexibility. It’s a novel approach to neural radiance fields that leverages multiple input images to synthesize novel views of complex scenes. It’s a transformative tool for 3D content creation and manipulation.
NeRFs can capture 3D scenes with high fidelity, but it does not offer explicit representations of shape, color, and material as meshes do. This makes it hard to distinguish and manipulate these attributes in the output. Furthermore, editing NeRF scenes by adding new objects requires view-consistency, which adds more complexity to the process.
Editing the output is as crucial as capturing the 3D scenes. Users can easily edit digital images and videos, especially with the recent text-to-X AI models that enable simple editing.
Blended-NeRF edits NeRF scenes with text or images for any region. It keeps the rest of the scene unchanged without new features or masks. The model produces realistic and consistent results that fit the scene. In addition, it works for any class or domain and allows complex text-based changes, such as object insertion/replacement, blending, and texture conversion.
Moreover, Blended-NeRF uses a language-image model, like CLIP, and a NeRF model trained on a NeRF scene to generate and blend new objects. The CLIP model guides the generation based on user input, creating diverse 3D objects that match the scene.
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Source: mPost
