As-Plausible-As-Possible: A Novel Mesh Deformation Technique Leveraging 2D Diffusion Priors for Visually Plausible Deformations

Conceptos Básicos

APAP, a novel mesh deformation framework, leverages 2D diffusion priors to produce visually plausible deformations of 2D and 3D triangular meshes while preserving the geometric properties of the original shape.

Resumen

The paper presents APAP, a novel mesh deformation framework that aims to produce visually plausible deformations of 2D and 3D triangular meshes. The key aspects of the framework are:

Representation of the mesh as a Jacobian field: The mesh is parameterized as a set of per-face Jacobians, which can be updated via gradient-descent optimization.

Incorporation of 2D diffusion priors: APAP leverages a pretrained 2D diffusion model (Stable Diffusion) to extract plausibility priors. The diffusion model is finetuned using LoRA to preserve the identity of the edited mesh.

Two-stage optimization: The framework consists of two stages - the first stage deforms the mesh based on user-specified constraints (handle displacements), while the second stage jointly optimizes the Jacobian field to balance the user constraints and the plausibility priors extracted from the diffusion model.

The paper evaluates APAP on both 3D and 2D mesh deformation tasks, demonstrating qualitative and quantitative improvements over baseline methods that rely solely on geometric priors. The experiments show that APAP can produce more plausible deformations while preserving the identity of the edited objects.

Estadísticas

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Citas

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Ideas clave extraídas de

As-Plausible-As-Possible

by Seungwoo Yoo... a las arxiv.org 04-02-2024

https://arxiv.org/pdf/2311.16739.pdf

Consultas más profundas

How can the proposed framework be extended to handle more complex mesh representations, such as quad-dominant meshes or subdivision surfaces?

In order to extend the proposed framework to handle more complex mesh representations like quad-dominant meshes or subdivision surfaces, several modifications and enhancements can be implemented:

Adaptation of Parameterization: Quad-dominant meshes and subdivision surfaces have different topological structures compared to triangular meshes. The framework can be modified to accommodate different parameterizations suitable for these types of meshes, such as quad-based parameterizations or hierarchical subdivision schemes.

Integration of Higher-order Elements: Quad-dominant meshes often contain higher-order elements like quad faces. The framework can be extended to support higher-order elements by incorporating appropriate interpolation schemes and deformation techniques tailored for these elements.

Refinement of Jacobian Field Representation: The Jacobian field representation used in the framework may need to be refined to handle the specific characteristics of quad-dominant meshes or subdivision surfaces. This could involve adapting the Jacobian computation method or introducing new constraints to preserve the geometric properties of these mesh types.

Enhanced Optimization Strategies: Complex mesh representations may require more sophisticated optimization strategies to ensure accurate and stable deformations. Techniques like hierarchical optimization, adaptive refinement, or multi-resolution approaches can be integrated into the framework to handle the intricacies of quad-dominant meshes and subdivision surfaces.

Incorporation of Subdivision Surface Rules: Subdivision surfaces have specific rules governing their behavior under deformation. The framework can be extended to incorporate these rules, ensuring that the deformed meshes maintain the desired subdivision properties and smoothness.

By incorporating these adaptations and enhancements, the proposed framework can be effectively extended to handle more complex mesh representations, providing a versatile tool for plausibility-aware deformation of a wide range of mesh types.

How can the diffusion prior be further improved to better capture the semantic and structural properties of the edited objects?

To enhance the diffusion prior for better capturing the semantic and structural properties of the edited objects, the following strategies can be considered:

Fine-tuning with Object-specific Data: The diffusion model can be fine-tuned using object-specific data to improve its understanding of the semantic and structural characteristics of different objects. By training the model on a diverse set of object instances, it can learn to capture object-specific features more effectively.

Multi-view Consistency: Incorporating multi-view consistency during training can help the diffusion model learn the structural properties of objects from different viewpoints. This can enhance the model's ability to capture 3D shape details and improve plausibility in deformations.

Semantic Embeddings: Introducing semantic embeddings or object class information into the diffusion model can guide the generation process based on the semantic content of the objects. This can help the model generate more contextually relevant deformations aligned with the object semantics.

Attention Mechanisms: Leveraging attention mechanisms within the diffusion model can enable it to focus on relevant parts of the object during deformation, improving the preservation of structural details and semantic consistency.

Adaptive Diffusion Steps: Implementing adaptive diffusion steps based on the complexity of the object or the editing instructions can help the model adjust its diffusion process to better capture the semantic and structural properties of the edited objects.

By incorporating these strategies, the diffusion prior can be further improved to enhance its ability to capture the semantic and structural properties of edited objects, leading to more realistic and contextually relevant deformations.

What other types of priors, beyond 2D diffusion models, could be integrated into the optimization framework to enhance the plausibility of the deformed meshes?

In addition to 2D diffusion models, several other types of priors can be integrated into the optimization framework to enhance the plausibility of deformed meshes:

Physical Constraints: Incorporating physical constraints such as material properties, gravity, and collision avoidance can improve the realism of deformations, ensuring that the edited meshes adhere to physical laws and constraints.

Shape Grammar Priors: Utilizing shape grammar priors can enforce specific rules and relationships between parts of the mesh, guiding the deformation process to generate structurally plausible shapes based on predefined grammar rules.

Texture and Appearance Priors: Integrating texture and appearance priors can help maintain the visual coherence of the deformed meshes by preserving texture details, color consistency, and surface appearance characteristics during editing.

User Interaction Priors: Considering user interaction priors, such as historical editing patterns or user preferences, can personalize the deformation process to align with user expectations and editing styles, enhancing the user experience and satisfaction.

Temporal Consistency Priors: Including temporal consistency priors can ensure that deformations across different frames or time steps exhibit smooth transitions and continuity, maintaining coherence in animations or dynamic deformations.

Semantic Segmentation Priors: Leveraging semantic segmentation priors can guide the deformation process based on the semantic segmentation of the mesh, allowing for more contextually relevant and semantically consistent edits.

By integrating these diverse types of priors into the optimization framework, the plausibility of deformed meshes can be significantly enhanced, leading to more realistic and contextually appropriate shape manipulations.