CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields (2112.05139v3)

Abstract: We present CLIP-NeRF, a multi-modal 3D object manipulation method for neural radiance fields (NeRF). By leveraging the joint language-image embedding space of the recent Contrastive Language-Image Pre-Training (CLIP) model, we propose a unified framework that allows manipulating NeRF in a user-friendly way, using either a short text prompt or an exemplar image. Specifically, to combine the novel view synthesis capability of NeRF and the controllable manipulation ability of latent representations from generative models, we introduce a disentangled conditional NeRF architecture that allows individual control over both shape and appearance. This is achieved by performing the shape conditioning via applying a learned deformation field to the positional encoding and deferring color conditioning to the volumetric rendering stage. To bridge this disentangled latent representation to the CLIP embedding, we design two code mappers that take a CLIP embedding as input and update the latent codes to reflect the targeted editing. The mappers are trained with a CLIP-based matching loss to ensure the manipulation accuracy. Furthermore, we propose an inverse optimization method that accurately projects an input image to the latent codes for manipulation to enable editing on real images. We evaluate our approach by extensive experiments on a variety of text prompts and exemplar images and also provide an intuitive interface for interactive editing. Our implementation is available at https://cassiepython.github.io/clipnerf/

Insightful Overview of "CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields"

This paper presents "CLIP-NeRF," a robust multi-modal framework for 3D object manipulation using Neural Radiance Fields (NeRF). The proposed approach fuses the versatile capabilities of Contrastive Language-Image Pre-Training (CLIP) with the inherently detailed representation of NeRF, facilitating intuitive 3D content manipulation through text prompts or exemplar images. The innovative methodology addresses significant challenges in NeRF manipulation by introducing a disentangled architecture that independently controls shape and appearance, offering a practical solution for interactive editing in various applications.

Disentangled Conditional NeRF Architecture

The authors introduce a disentangled conditional NeRF architecture that segregates the influence on shape and appearance. This separation is crucial for precise control and enhancement of NeRF’s editing capabilities. The architecture utilizes a shape deformation network that achieves shape manipulation through conditioned volumetric deformations, maintaining the original shape's integrity and preventing unwanted appearance changes. In contrast, appearance conditioning is aptly deferred to the rendering stage, ensuring isolated adjustments to color and texture attributes.

CLIP Integration for Modal Manipulation

The paper capitalizes on the powerful multimodal space provided by the CLIP model, utilizing it to bridge textual or visual input with the NeRF latent space. By employing CLIP, the authors develop shape and appearance mappers that translate language or image features into actionable transformations within the NeRF framework. This integration enables seamless editing through a straightforward interface of text prompts or visual exemplars, considerably simplifying the manipulation process and making it accessible even to non-specialists.

Empirical Evaluation and Numerical Results

The authors conduct extensive experiments to evaluate the performance of CLIP-NeRF against existing techniques, particularly focusing on the capability of the framework to deliver reliable manipulations while preserving image quality. The quantitative assessments demonstrate significant improvements in manipulation precision and fidelity, especially when compared to the benchmark EditNeRF. The framework not only excels in manipulation tasks but also shows remarkable efficiency in terms of computational resources, significantly reducing the time required for real-time editing operations.

Implications and Future Directions

The implications of this research extend beyond technical enhancements to NeRF manipulation. The introduction of CLIP-NeRF can potentially transform workflows in domains requiring detailed 3D modeling, such as augmented reality, virtual environment synthesis, and digital content creation. The disentangled approach opens pathways for further refinement, where more nuanced manipulation, even on fine-grained details, can be realized. Future work could explore the incorporation of more extensive datasets and advanced learned representations to overcome current limitations, offering even greater flexibility and scope in 3D rendering and manipulation tasks.

Conclusion

"CLIP-NeRF" stands as a significant contribution to the field of implicit scene representation and manipulation, offering a practical, flexible, and user-friendly approach for controlling complex 3D environments. By effectively leveraging the integration of CLIP with NeRF, this framework reveals new potentials for interactive and intuitive 3D content generation and manipulation. As AI and machine learning advance, the methodologies proposed in this paper set a precedent for future research directions in neural rendering and multi-modal machine learning applications.