3D-Fixup: Advancing Photo Editing with 3D Priors (2505.10566v1)

Abstract: Despite significant advances in modeling image priors via diffusion models, 3D-aware image editing remains challenging, in part because the object is only specified via a single image. To tackle this challenge, we propose 3D-Fixup, a new framework for editing 2D images guided by learned 3D priors. The framework supports difficult editing situations such as object translation and 3D rotation. To achieve this, we leverage a training-based approach that harnesses the generative power of diffusion models. As video data naturally encodes real-world physical dynamics, we turn to video data for generating training data pairs, i.e., a source and a target frame. Rather than relying solely on a single trained model to infer transformations between source and target frames, we incorporate 3D guidance from an Image-to-3D model, which bridges this challenging task by explicitly projecting 2D information into 3D space. We design a data generation pipeline to ensure high-quality 3D guidance throughout training. Results show that by integrating these 3D priors, 3D-Fixup effectively supports complex, identity coherent 3D-aware edits, achieving high-quality results and advancing the application of diffusion models in realistic image manipulation. The code is provided at https://3dfixup.github.io/

• The paper introduces 3D-Fixup, a novel framework that leverages 3D priors and diffusion models trained on video data to enable realistic 3D transformations of objects in 2D images.
• It employs a conditional diffusion model with dual 2D and 3D guidance within an efficient feed-forward architecture, offering a scalable alternative to expensive optimization methods.
• Evaluations show 3D-Fixup outperforms prior methods in fidelity and realism, handling complex transformations like out-of-plane rotations and multi-step edits robustly for practical applications.

Evaluation of 3D-Fixup: Advancing Photo Editing with 3D Priors

The paper "3D-Fixup: Advancing Photo Editing with 3D Priors" introduces an innovative approach to image editing by integrating 3D priors with generative models, particularly diffusion models. The research addresses the longstanding challenge of realistically editing 2D images by leveraging learned 3D priors, enabling complex transformations such as 3D rotation and translation of objects within images. The authors propose a novel framework that utilizes video data to capture real-world physical dynamics, creating a robust training dataset that allows a model to perform identity-coherent and quality-enhanced 3D edits.

Overview and Architectural Contributions

The paper outlines the following substantive innovations:

1. Data Generation Pipeline: One of the key hurdles in the domain of 3D-aware image editing is the availability of high-quality datasets that can capture realistic 3D transformations. The authors introduce a data generation pipeline built on video data that systematically provides pairs of source and target frames. These frames are enriched with 3D priors obtained via Image-to-3D models that facilitate the projection of 2D information into 3D space.
2. Conditional Diffusion Model: The research employs a conditional diffusion framework where the diffusion process is conditioned not only on standard 2D data but also incorporates guidance from 3D transformations. This dual-guidance system, exemplified in the paper's 3D-Fixup model, enhances the model's capability to reconstruct realistic 3D object transformations while preserving fine details, identity, pose, and lighting conditions.
3. Efficient Feed-Forward Architecture: The paper contrasts their method against optimization-based approaches that, while accurate, are computationally expensive. By leveraging feed-forward deep networks, 3D-Fixup offers a solution that is both efficient and scalable, making it practical for real-world applications.

Key Results and Comparative Analysis

The empirical evaluations conducted within the paper point to significant improvements over existing methodologies. The model outperforms current state-of-the-art techniques in both fidelity and realism of edits, achieving lower values in LPIPS and FID metrics, indicative of better perceptual similarity and realism. Specifically, the proposed framework exhibits robustness to transformations that are difficult for prior models, including out-of-plane rotations and translations, as well as handling multi-step rotations effectively.

Implications and Future Directions

The ability to manipulate images reliably with 3D-aware transformations has broader implications across several sectors. In e-commerce, for instance, products can be demonstrated from various perspectives without the need for multiple distinct photographs. Similarly, in media production, artists can reconfigure scenes in ways that were previously not possible without fully reconstructing 3D environments. The scalability and efficiency of the proposed feed-forward model make these types of edits accessible and practical in real-world implementations.

The paper suggests that extending the model to handle scenes with multiple interacting objects could be a valuable direction, potentially involving enhanced multi-object reconstruction capabilities. Additionally, refining the structural 3D priors and exploring their integration with more nuanced physical models or simulations of environmental effects such as lighting or occlusions could significantly improve realism and applicability across datasets.

In conclusion, the 3D-Fixup model represents a significant step in bridging the gap between theoretical research in generative modeling and practical applications in image editing, emphasizing the role of 3D priors in achieving photorealistic and coherent transformations. This paper thus contributes substantively to the field, providing a foundation for future research into more complex and precise digital transformation tasks.