MagicClay: Sculpting Meshes With Generative Neural Fields (2403.02460v4)

Abstract: The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.

• The paper introduces MagicClay, a hybrid model that combines mesh and SDF representations to enhance localized control in 3D shape generation.
• It utilizes differentiable mesh reconstruction to optimize topology, balancing intuitive artistic edits with robust global deformations.
• Comparative experiments show superior geometric fidelity under gradient noise, indicating significant advancements for 3D design and modeling.

An Overview of "MagicClay: Sculpting Meshes With Generative Neural Fields"

The paper "MagicClay: Sculpting Meshes With Generative Neural Fields" proposes a hybrid approach to 3D shape generation that marries the advantages of triangular mesh and Signed Distance Field (SDF) representations. This combination is designed to overcome the limitations inherent in existing methodologies, particularly concerning localized control and the efficiency of representation transformations. This research introduces MagicClay, a tool aimed at facilitating localized and incremental artistic edits guided by textual prompts, thereby bridging a gap in current 3D modeling workflows.

Core Framework and Methodology

The authors propose a system that concurrently maintains both a mesh and an SDF representation throughout the optimization process. The mesh is utilized primarily for its intuitive manipulation capabilities favored by artists, while the SDF representation provides robustness and efficiency for complex shape transformations required by generative tasks. This dual approach allows the system to benefit from both representations: the global deformation capabilities of SDFs and the localized control characteristic of mesh models.

MagicClay's architecture leverages differentiable mesh reconstruction, which adaptively manages the topology of the mesh based on the evolving structural needs as indicated by the SDF. The mesh assists in efficiently localizing high-resolution SDF rendering by concentrating sampling efforts around the mesh surface. This method significantly reduces the computational costs typically associated with volumetric rendering.

Numerical Results and Comparative Analysis

The paper presents a series of comparative experiments against state-of-the-art generative models such as Fantasia3D, ProlificDreamer, and TextMesh. The results reveal that MagicClay demonstrates superior capabilities in maintaining the geometric fidelity of generated shapes, which is particularly evident in the clarity of the extracted meshes absent detailed textures. The empirical evaluations suggest that the hybrid system outperforms pure mesh-based or SDF-based techniques in environments where gradient noise is a challenge, a common scenario in applications reliant on Score-Distillation Sampling (SDS).

Implications and Future Prospects

The implications of this research are significant for both theoretical exploration and practical applications. MagicClay presents a potential shift in digital sculpting tools, where designers and artists can insert more semantic input into the design process seamlessly. The theoretical implications extend to discussions on hybrid representations in neural fields and suggest avenues for further investigating combined representations for other domains beyond 3D shape generation.

In the future, research could explore the integration of MagicClay's methodologies within commercial 3D modeling suites, potentially reducing the barrier to entry for 3D artists. Another area for development might be enhancing the interactive speed of the system, making it feasible for real-time applications, especially in environments that require frequent or continuous updates such as virtual reality and augmented reality. Further refinements could involve leveraging advancements in diffusion models to enhance the consistency and quality of 3D reconstructions.

In conclusion, the MagicClay framework demonstrates a promising direction in the evolution of generative 3D modeling by uniting the robustness and control provided by distinct 3D representations. It opens pathways for 3D design tools to be more accessible and expressive, offering vast potential for enhancement in artistic workflows and generative design tasks.