NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs (2402.08622v1)

Abstract: A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way, such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end, we generalize classic image analogies from 2D images to NeRFs. We leverage correspondence transfer along semantic affinity that is driven by semantic features from large, pre-trained 2D image models to achieve multi-view consistent appearance transfer. Our method allows exploring the mix-and-match product space of 3D geometry and appearance. We show that our method outperforms traditional stylization-based methods and that a large majority of users prefer our method over several typical baselines.

• The paper introduces a novel approach that leverages dense semantic feature mapping to transfer visual appearance from a source NeRF to a geometry-only target.
• It employs DiNO-ViT based feature extraction and cosine-similarity mapping to ensure semantic consistency and high-quality detail preservation.
• Quantitative metrics and qualitative studies confirm the method outperforms traditional stylization techniques, enhancing virtual content creation and 3D modeling.

Exploring NeRF Analogies for Semantic Appearance Transfer

Introduction to NeRF Analogies

The paper presents a novel approach within the domain of Neural Radiance Fields (NeRFs), specifically focusing on the semantic transfer of visual attributes between different NeRFs. Given a source NeRF that encapsulates the geometry and appearance of an object, alongside a target NeRF that contains purely geometric information, the proposed method effectively transfers the appearance of the source onto the target. This process maintains the geometric integrity of the target while adopting the appearance characteristics of the source, thereby creating what the authors term as NeRF analogies.

Methodology and Approach

Feature Extraction and Mapping

A core component of creating NeRF analogies involves extracting dense feature descriptors using a DiNO-ViT, a pre-trained vision transformer known for its effectiveness in capturing semantic and structural details across images. These descriptors enable the identification of semantically similar regions between the source and target NeRFs, facilitating accurate appearance transfer. The method employs a mapping mechanism, where features from the target are aligned with those from the source based on semantic affinity, measured through cosine similarity. This mapping is crucial for transferring the visual attributes while preserving the semantic context.

Training & Implementation Details

The neural network utilizes a standard NeRF architecture augmented with an edge loss control to refine the visual fidelity in high-frequency regions. Training parameters and optimization strategies are meticulously chosen to balance efficiency and output quality. The edge loss, in particular, addresses potential inconsistencies in feature correspondences by emphasizing detail preservation around object edges.

Results and Evaluation

Quantitative and Qualitative Assessments

The paper thoroughly evaluates the proposed NeRF analogies through both quantitative metrics and a user paper. It significantly outperforms traditional stylization and image-analogy methods across various metrics, including PSNR, SSIM, and CLIP direction consistency. Qualitative comparisons further underscore the method's ability to generate multi-view consistent and semantically coherent visual attribute transfers.

Ablation Studies and Further Experiments

The authors also explore the impact of feature extraction techniques, comparing DiNO-ViT with alternatives like SIFT, and extend their methodology to different input modalities like signed distance fields (SDFs). These experiments highlight the robustness and flexibility of the approach, endorsing its effectiveness beyond standard NeRF representations.

Implications and Future Directions

The successful development and validation of NeRF analogies introduce a compelling avenue for semantic appearance editing in three-dimensional spaces. By facilitating intuitive and accurate visual attribute transfers, this research could significantly benefit fields like virtual content creation, 3D modeling, and augmented reality. Looking forward, extensions of this work could explore texture transfer and the manipulation of intrinsic scene parameters, unlocking even more nuanced editing capabilities.

Conclusion

This paper expands the horizons of NeRF manipulations by introducing the concept of NeRF analogies, a sophisticated methodology for semantically meaningful appearance transfer between different NeRFs. Through meticulous experimentation and evaluation, the authors have laid a strong foundation for future advancements in three-dimensional visual attribute editing, promising a rich landscape of opportunities for both research and practical applications in the field of generative AI.