AnchorFlow: Training-Free 3D Editing via Latent Anchor-Aligned Flows

Abstract

This paper introduces AnchorFlow, a novel training-free approach for high-quality 3D object editing by leveraging latent anchor-aligned flows. We propose a method to manipulate 3D representations by defining latent space anchors, enabling intuitive and disentangled control over various object attributes without extensive retraining. Our technique demonstrates superior efficiency and versatility compared to existing methods, achieving photorealistic and consistent 3D edits. The system significantly reduces the computational burden traditionally associated with 3D content generation and manipulation.

1. Introduction

The creation and manipulation of high-quality 3D content remain challenging, often requiring specialized skills and substantial computational resources. Existing methods for 3D editing frequently involve costly retraining or complex optimization processes, limiting their practicality and accessibility. This work addresses the need for an intuitive and efficient training-free 3D editing framework. The models used in this article are primarily built upon pre-trained 2D diffusion models and Neural Radiance Fields (NeRFs) or similar 3D generative architectures.

2. Related Work

Prior research in 3D content generation has explored various avenues, including explicit mesh modeling, implicit neural representations like NeRFs, and 3D-aware generative adversarial networks. Latent space editing techniques, often derived from 2D image synthesis, have shown promise in disentangling attributes but struggle with 3D consistency and generalizability. While some methods offer training-free image editing, extending these to coherent 3D manipulation without re-optimization remains a significant hurdle that AnchorFlow aims to overcome by aligning latent flows.

3. Methodology

AnchorFlow operates by establishing a set of 'anchors' within the latent space of a pre-trained 3D generative model, which represent distinct editing attributes or states. The method then defines latent flows that align with these anchors, allowing for semantic manipulations without requiring additional training or fine-tuning of the base model. This involves inverse mapping real 3D objects into the latent space and then applying flow-guided transformations based on user-defined anchors. The approach ensures coherent 3D edits by maintaining consistency across different views and resolutions.

4. Experimental Results

Experimental results demonstrate AnchorFlow's capability to perform diverse and high-fidelity 3D edits, including shape deformation, material alteration, and texture modification. Quantitative metrics show significant improvements in editing efficiency and visual quality compared to several baseline methods, particularly in maintaining geometric consistency and semantic integrity. Qualitative comparisons further highlight the intuitive control and photorealistic output achievable with our training-free framework. The following table summarizes the performance metrics against competing methods, showing AnchorFlow's superior balance of quality and speed. Comparisons against other methods like StyleCLIP for 3D and various optimization-based NeRF editing techniques demonstrate AnchorFlow's competitive edge in terms of both visual fidelity and inference speed, making it suitable for interactive applications.

5. Discussion

AnchorFlow successfully addresses the challenges of training-free 3D editing, offering a robust and efficient solution for manipulating complex 3D scenes and objects. The interpretability of latent anchors and the semantic guidance of flows provide users with fine-grained control over edits, opening new possibilities for interactive 3D content creation. Future work includes extending AnchorFlow to accommodate more complex scene compositions and exploring its integration with real-time rendering pipelines for enhanced user experiences.