ShapeFusion: A Diffusion-based 3D Model for Localized and Disentangled Shape Editing

Core Concepts

The proposed ShapeFusion model leverages diffusion models to enable diverse and fully localized edits on 3D meshes, while preserving the unedited regions. It introduces an effective diffusion masking training strategy that facilitates localized manipulation of any shape region, without being limited to predefined regions or sparse control vertices.

Abstract

The paper introduces ShapeFusion, a 3D diffusion model for localized and disentangled shape editing. The key contributions are:

A simple but effective training strategy for diffusion models that learns local priors of the underlying data distribution, enabling superior localized shape manipulations compared to traditional VAE architectures.

A localized 3D model that enables direct point manipulation, sculpting, and expression editing directly on the 3D space. ShapeFusion guarantees fully localized editing of user-selected regions and provides an interpretable paradigm compared to methods relying on latent code manipulation.

Experiments show that ShapeFusion not only generates diverse region samples that outperform current state-of-the-art models, but also learns strong priors that can substitute current parametric models for tasks like reconstruction and global sampling.

The paper first introduces the forward diffusion process and the denoising module based on hierarchical mesh convolutions. It then evaluates the model's performance on localized region sampling, direct point manipulation, global sampling, and expression editing, demonstrating superior results compared to baseline methods.

Stats

The proposed method was trained and evaluated on three datasets: UHM, STAR, and MimicMe, which contain 3D human faces and bodies.

Quotes

"Following our framework, a user can explicitly set their manipulation region of choice and define an arbitrary set of vertices as handles to edit a 3D mesh."
"Compared to the current state-of-the-art our method leads to more interpretable shape manipulations than methods relying on latent code state, greater localization and generation diversity while offering faster inference than optimization based approaches."

Key Insights Distilled From

ShapeFusion

by Rolandos Ale... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19773.pdf

Deeper Inquiries

How can the proposed method be extended to handle dynamic 3D shapes, such as facial expressions or body animations?

The proposed method can be extended to handle dynamic 3D shapes by incorporating temporal information into the diffusion process. For facial expressions, the model can be trained on sequences of 3D facial scans capturing different expressions. By introducing a temporal component to the diffusion process, the model can learn to generate dynamic shape variations over time. This can enable the manipulation of facial expressions in a localized manner, allowing for precise control over specific regions of the face during dynamic expressions. Similarly, for body animations, the model can be trained on sequences of 3D body scans in different poses or movements. By incorporating temporal dynamics into the diffusion process, the model can generate realistic and dynamic body animations while preserving the localized editing capabilities.

What are the potential applications of the localized shape editing capabilities of ShapeFusion beyond computer graphics and virtual avatars?

The localized shape editing capabilities of ShapeFusion have a wide range of potential applications beyond computer graphics and virtual avatars. Some of these applications include:

Medical Imaging: ShapeFusion can be used in medical imaging for precise manipulation of 3D anatomical models, allowing for localized editing of specific regions of interest in medical scans. This can aid in surgical planning, patient-specific treatment design, and educational purposes.
Industrial Design: In industrial design, ShapeFusion can be utilized for customized product design and prototyping. Designers can manipulate specific regions of 3D models to optimize product features and functionalities.
Fashion and Apparel: ShapeFusion can be applied in the fashion industry for personalized clothing design. Designers can edit and customize specific areas of garments to fit individual preferences and body shapes accurately.
Forensics and Anthropology: In forensic investigations and anthropological studies, ShapeFusion can assist in the analysis and reconstruction of 3D facial features or skeletal structures with precise localized editing capabilities for facial reconstructions or age progression.
Art and Sculpture: Artists and sculptors can benefit from ShapeFusion for creating intricate and detailed 3D sculptures with localized manipulation of shapes, allowing for artistic expression and creativity in digital sculpting.

Can the diffusion-based approach be adapted to handle other 3D data representations, such as point clouds or implicit surfaces, while maintaining the localized editing properties?

Yes, the diffusion-based approach can be adapted to handle other 3D data representations such as point clouds or implicit surfaces while maintaining the localized editing properties. For point clouds, the diffusion process can be modified to operate on individual points in the cloud, gradually introducing noise and predicting denoised versions of the points. By incorporating a masking strategy similar to the one used for meshes, the model can learn localized features and enable precise point cloud editing in specific regions.
Similarly, for implicit surfaces, the diffusion model can be extended to operate on the implicit surface representation. The model can gradually introduce noise to the implicit surface function and predict the denoised surface, allowing for localized editing of the implicit surface in specific regions. By adapting the hierarchical mesh convolution layers to work with implicit surfaces, the model can maintain its ability to perform localized manipulations while handling different 3D data representations.