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Consolidating Attention Features for Multi-view Image Editing (2402.14792v1)

Published 22 Feb 2024 in cs.CV, cs.GR, and cs.LG

Abstract: Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view images depicting a single scene leads to 3D-inconsistent results. In this work, we focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views. We build on two insights: (1) maintaining consistent features throughout the generative process helps attain consistency in multi-view editing, and (2) the queries in self-attention layers significantly influence the image structure. Hence, we propose to improve the geometric consistency of the edited images by enforcing the consistency of the queries. To do so, we introduce QNeRF, a neural radiance field trained on the internal query features of the edited images. Once trained, QNeRF can render 3D-consistent queries, which are then softly injected back into the self-attention layers during generation, greatly improving multi-view consistency. We refine the process through a progressive, iterative method that better consolidates queries across the diffusion timesteps. We compare our method to a range of existing techniques and demonstrate that it can achieve better multi-view consistency and higher fidelity to the input scene. These advantages allow us to train NeRFs with fewer visual artifacts, that are better aligned with the target geometry.

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Citations (2)
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Summary

  • The paper introduces QNeRF, which consolidates query features during diffusion to ensure consistent multi-view edits.
  • The method leverages progressive refinement of self-attention features for precise 3D geometric control.
  • Benchmark tests reveal lower KID and FID scores and improved user ratings, demonstrating superior visual fidelity.

Consolidating Attention Features for Multi-view Image Editing

The paper "Consolidating Attention Features for Multi-view Image Editing" addresses the challenge of achieving consistent multi-view image editing using 3D geometric control. Traditional image editing techniques utilizing large-scale text-to-image models often fail to produce consistent edits when applied to multiple images of the same scene, especially when these involve complex geometric adjustments. The authors propose a novel approach that consolidates attention features in diffusion models to maintain consistency across various views during spatial control-based geometric manipulations.

Summary of Methodology

The researchers develop a method that leverages two key insights: the importance of maintaining consistent features throughout the generative process, and the significant influence of queries in self-attention layers on image structure. They introduce QNeRF (Query Neural Radiance Field), which is a neural radiance field trained on internal query features from self-attention layers. This QNeRF is capable of rendering 3D-consistent queries that are progressively consolidated during the image generation process. The methodology involves a progressive, iterative refinement technique, where QNeRF consolidates query features at various diffusion steps, offering a consistent representation that guides the generation of edited images.

Key Numerical Results and Comparisons

The authors compare their method with various baseline techniques, including Instruct-NeRF2NeRF (IN2N) enhanced with ControlNet, collaborative score distillation (CSD), and TokenFlow for video generation, all of which are adapted to incorporate spatial controls akin to ControlNet. Their approach achieves superior multi-view consistency and fidelity to the original scenes compared to these baseline methods. This is quantitatively supported by evaluations such as Kernel Inception Distance (KID) and Fréchet Inception Distance (FID), where their method shows improved fidelity to the original scenes and a higher preference in user studies for 3D alignment and visual quality. Specifically, their method yielded a lower KID and FID than the alternatives and was the preferred choice in user evaluations concerning alignment and quality of the resulting 3D representations.

Implications and Future Directions

The proposed method has significant implications for enhancing 3D consistency in multi-view image editing, particularly for applications requiring precise geometric alterations. By refining the consistency of attention-based features, the approach addresses fundamental limitations in current multi-view editing techniques. This opens new avenues where advanced 3D-consistent editing could be applied more seamlessly in areas such as virtual reality content creation, advanced scene modeling, and interactive design.

Future research could explore further refinement of feature consistency, possibly integrating higher resolution feature alignment to tackle issues with detailed textures and backgrounds. Exploring alternative 3D representations such as Gaussian splats could also provide different consolidation mechanisms and improve computational efficiency. Additionally, extending this framework to handle more generalized scenarios involving dynamic scenes or real-time editing could broaden the applicability and robustness of multi-view image editing models.

Overall, this paper presents a substantial contribution to the field of multi-view image editing, offering a path forward in achieving consistent geometric transformations across multiple views through innovative attention feature consolidation techniques.

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