MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors (2410.16272v1)

Abstract: Drag-based editing has become popular in 2D content creation, driven by the capabilities of image generative models. However, extending this technique to 3D remains a challenge. Existing 3D drag-based editing methods, whether employing explicit spatial transformations or relying on implicit latent optimization within limited-capacity 3D generative models, fall short in handling significant topology changes or generating new textures across diverse object categories. To overcome these limitations, we introduce MVDrag3D, a novel framework for more flexible and creative drag-based 3D editing that leverages multi-view generation and reconstruction priors. At the core of our approach is the usage of a multi-view diffusion model as a strong generative prior to perform consistent drag editing over multiple rendered views, which is followed by a reconstruction model that reconstructs 3D Gaussians of the edited object. While the initial 3D Gaussians may suffer from misalignment between different views, we address this via view-specific deformation networks that adjust the position of Gaussians to be well aligned. In addition, we propose a multi-view score function that distills generative priors from multiple views to further enhance the view consistency and visual quality. Extensive experiments demonstrate that MVDrag3D provides a precise, generative, and flexible solution for 3D drag-based editing, supporting more versatile editing effects across various object categories and 3D representations.

• The paper introduces a novel drag-based 3D editing method leveraging multi-view diffusion models to enable coherent topology manipulation.
• It integrates multi-view generated reconstructions with 3D Gaussian representations using view-specific deformation networks for accurate results.
• Experimental evaluations show improved dragging accuracy and perceptual realism, outperforming state-of-the-art mesh and Gaussian editing techniques.

An Evaluation of MVDrag3D: A Framework for Creative 3D Editing Utilizing Multi-View Generation-Reconstruction Priors

The presented work introduces a novel approach, MVDrag3D, to the challenge of drag-based 3D editing, which has traditionally been constrained by limited topology manipulation capabilities in existing frameworks. MVDrag3D leverages multi-view generation and reconstruction priors to extend the flexibility and effectiveness of 3D editing processes, aiming for enhanced accuracy, generative capabilities, and versatility across various object categories and 3D representations.

Core Contributions and Methodology

The paper notes the underlying complexity of adapting drag-based techniques, prevalent in 2D media due to image generative models, into the 3D context. Traditional 3D methods, like mesh deformations, face limitations in handling significant topology changes or creating new textures across diverse categories. MVDrag3D addresses these limitations through several strategic components:

1. Multi-view Diffusion Models: Positioned as strong generative priors, these models facilitate consistent drag editing across multiple rendered views. This approach reflects insights from successful 2D generative models but extends them into a multi-dimensional space crucial for 3D coherence. The system's architecture supports not just mesh-based modeling but also more flexible representations like 3D Gaussians.
2. Fusion of Views into 3D Gaussians: Following the multi-view editing, a 3D Gaussian reconstruction model aggregates these views into a holistic 3D model. This model initially suffers from potential misalignments across views. However, these are corrected through view-specific deformation networks, a noteworthy innovation in ensuring coherence and alignment.
3. Multi-view Score Function: To increase consistency and visual quality, a multi-view score function is proposed. This mechanism ties together generative priors derived from multiple views into a cohesive editing experience, simultaneously improving fidelity and maintaining detail integrity across views.

Experimental Insights and Comparative Evaluation

The experimental results provided are substantial. The paper claims that MVDrag3D effectively surpasses the current state-of-the-art, including methods like Drag3D, in its performance across 3D editing tasks, whether operating on meshes or Gaussians. These assertions are supported by extensive quantitative analysis using metrics like Dragging Accuracy Index (DAI) and GPTEval3D. Specifically, tests highlight marked improvements in both the accuracy of drag operations and the perceptual realism of resultant structures.

The introduction of MVDrag3D is contextualized within the broader paradigm shift towards creative, user-driven content creation in 3D environments. The approach not only maintains fine-grained control over the spatial manipulation of objects but also ensures computational efficiency and rendering quality, addressing a diverse array of objects and 3D forms without requiring labor-intensive, object-specific modeling adjustments.

Implications and Future Directions

This research underscores the potential of integrating generative reconstruction models with adaptable editing interfaces, a trend gaining traction as the scope and applicability of 3D modeling continue expanding. Practically, MVDrag3D stands to lower barriers for non-expert users in complex 3D design tasks, facilitating more intuitive creativity without sacrificing precision or quality.

The paper prepares the field for future explorations regarding further reducing inversion inconsistencies and enhancing real-time feedback mechanisms, potentially through advancements in diffusion modeling and AI-driven perceptual assessments.

MVDrag3D represents a sophisticated blend of technological innovation with user-centric design, setting a promising foundation for future reconfigurations of nuanced, generative 3D editing solutions. The insights encapsulated here form a critical node in the evolving narrative of computational graphics and artificial intelligence, urging more seamless integration of 2D intuitiveness into the inherently complex 3D modeling space.