The 3D computer vision domain has been inundated with NeRF in recent years. They emerged as a revolutionary technique and enabled the reconstruction and synthesis of new visions of a scene. NeRFs capture and model underlying geometry and appearance information from a collection of multi-view images.
Leveraging neural networks, NeRFs offer a data-driven approach that surpasses traditional methods. Neural networks in NeRFs learn to represent the complex relationship between scene geometry, lighting, and view-dependent appearance, enabling highly detailed and realistic scene reconstructions. The primary advantage of NeRFs lies in their ability to generate photorealistic images from any desired viewpoint within a scene, even in regions that were not captured by the original image set.
The success of NeRFs has opened up new possibilities in computer graphics, virtual reality, and augmented reality, enabling the creation of immersive and interactive virtual environments that closely resemble real-world scenes. Therefore, there is serious interest in the domain to advance NeRFs further.
Some drawbacks of NeRFs limit their applicability in real-world scenarios. For example, the modification of neural fields is a significant challenge due to the implicit coding of shape and texture information within the features of the high-dimensional neural network. While some methods have tried to address this using explored editing techniques, they often require a lot of user input and struggle to produce high-quality, accurate results.
The ability to modify NeRFs can open up possibilities in real-world applications. However, so far, all attempts have not been enough to solve the problems. Well, we have a new player in the game, and his name is Dream editor.
Dream editor is an intuitive framework that allows intuitive and convenient editing of neural fields using text prompts. By representing the scene with a mesh-based neural field and employing a phased editing framework, Dream editor allows for a wide range of editing effects, including re-texturing, object replacement, and object insertion.
Mesh representation facilitates precise local editing by converting 2D editing masks into 3D editing regions while simultaneously untangling geometry and textures to avoid excessive deformation. The phased framework combines pre-trained diffusion models with score distillation sampling, enabling efficient and accurate editing based on simple text prompts.
Dream editor follows three key steps to facilitate intuitive and accurate text-driven 3D scene editing. In the initial stage, the original neural radiance field is transformed into a mesh-based neural field. This mesh representation allows for spatially selective editing. After conversion, it uses a custom Text-to-Image (T2I) model that is trained on the specific scene to capture the semantic relationships between keywords in text prompts and the visual content of the scene. Finally, the modified changes are applied to the target object within the neural field using the T2I diffusion mode.
Dream editor it can accurately and progressively modify the 3D scene while maintaining a high level of fidelity and realism. This step-by-step approach, from mesh-based representation to precise localization and controlled editing through diffusion models, enables it Dream editor to achieve highly realistic editing results by minimizing unnecessary editing in irrelevant regions.
Check out thePaper. Don’t forget to joinour 25k+ ML SubReddit,Discord channel,ANDEmail newsletterwhere we share the latest news on AI research, cool AI projects, and more. If you have any questions regarding the above article or if you have missed anything, please do not hesitate to email us atAsif@marktechpost.com
Ekrem Cetinkaya received his B.Sc. in 2018 and M.Sc. in 2019 at Ozyegin University, Istanbul, Trkiye. She wrote her M.Sc. thesis on image denoising by deep convolutional networks. She holds a PhD. he graduated in 2023 from the University of Klagenfurt, Austria with his thesis entitled “Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning”. His research interests include deep learning, computer vision, video coding and multimedia networking.
#pump #dimensions #DreamEditor #model #edits #scenes #text #prompts
Image Source : www.marktechpost.com