Generating High-Quality Textured 3D Assets with Intrinsic Diffusion Models

Core Concepts

We introduce a framework for intrinsic latent diffusion models operating directly on the surfaces of 3D shapes, with the goal of synthesizing high-quality textures. Our approach consists of two key components: Field Latents, a latent representation encoding textures as discrete vector fields on the mesh vertices, and Field Latent Diffusion Models, which learn to denoise a diffusion process in the learned latent space on the surface.

Abstract

The authors present a novel framework for generating high-quality textured 3D assets using intrinsic latent diffusion models. The key contributions are: Field Latents (FL): A latent representation that encodes textures as discrete vector fields on the mesh vertices, capturing directional information about the local texture. Field Latent Diffusion Models (FLDMs): Diffusion models that learn to denoise a diffusion process in the FL latent space on the surface, leveraging field convolutions to preserve equivariance under isometries. The authors apply this framework in a single-textured-mesh setting, where the models are trained to generate variations of a given texture on a mesh. They demonstrate the ability to synthesize high-fidelity textures, perform label-guided generation, inpainting, and generative texture transfer by sampling pre-trained FLDMs on new, similar meshes. The authors show that their approach outperforms existing single-textured-mesh generative models in terms of texture reconstruction quality and fidelity of unconditionally generated samples. The isometry-equivariance of the proposed representations and models enables seamless reproduction of textural details across locally similar regions of the mesh, and facilitates the notion of generative texture transfer.

Stats

The authors use the following datasets in their experiments: Google Scanned Objects dataset [13] for texture reconstruction evaluation Objaverse [8] and Scanned Objects [13] datasets for unconditional texture generation

Quotes

"Our latent diffusion models operate directly on the surfaces of 3D shapes, synthesizing new high-quality textures (center) after training on a single example (left)." "Both our novel latent representation and diffusion models are isometry-equivariant, facilitating a notion of generative texture transfer by sampling pre-trained models on new geometries (right)."

Key Insights Distilled From

Single Mesh Diffusion Models with Field Latents for Texture Generation

by Thomas W. Mi... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2312.09250.pdf

Deeper Inquiries

How could the proposed framework be extended to handle more complex 3D geometries beyond single-textured meshes, such as multi-material objects or articulated models?

To extend the framework to handle more complex 3D geometries, such as multi-material objects or articulated models, several modifications and additions could be made. Multi-Material Objects: Introducing a mechanism to handle multiple textures or materials on the same mesh. This could involve extending the field latent representation to encode information about different materials or textures present on the surface. Adapting the denoising process in the Field Latent Diffusion Models (FLDMs) to account for multiple textures or materials and their interactions. This could involve conditioning the denoising process on the specific material properties or textures. Training the FLDMs on datasets that include multi-material objects to learn the relationships between different textures and how they interact on the surface. Articulated Models: Incorporating information about the articulation or movement of different parts of the model. This could involve encoding information about joint angles or movement constraints in the latent representation. Modifying the denoising process in FLDMs to take into account the articulated nature of the model. This could involve conditioning the denoising process on the specific articulation parameters. Training the FLDMs on datasets that include articulated models to learn how textures vary with different articulations and movements.

How could the proposed framework be extended to handle more complex 3D geometries beyond single-textured meshes, such as multi-material objects or articulated models?

To extend the framework to handle more complex 3D geometries, such as multi-material objects or articulated models, several modifications and additions could be made. Multi-Material Objects: Introducing a mechanism to handle multiple textures or materials on the same mesh. This could involve extending the field latent representation to encode information about different materials or textures present on the surface. Adapting the denoising process in the Field Latent Diffusion Models (FLDMs) to account for multiple textures or materials and their interactions. This could involve conditioning the denoising process on the specific material properties or textures. Training the FLDMs on datasets that include multi-material objects to learn the relationships between different textures and how they interact on the surface. Articulated Models: Incorporating information about the articulation or movement of different parts of the model. This could involve encoding information about joint angles or movement constraints in the latent representation. Modifying the denoising process in FLDMs to take into account the articulated nature of the model. This could involve conditioning the denoising process on the specific articulation parameters. Training the FLDMs on datasets that include articulated models to learn how textures vary with different articulations and movements.

How could the proposed framework be extended to handle more complex 3D geometries beyond single-textured meshes, such as multi-material objects or articulated models?

To extend the framework to handle more complex 3D geometries, such as multi-material objects or articulated models, several modifications and additions could be made. Multi-Material Objects: Introducing a mechanism to handle multiple textures or materials on the same mesh. This could involve extending the field latent representation to encode information about different materials or textures present on the surface. Adapting the denoising process in the Field Latent Diffusion Models (FLDMs) to account for multiple textures or materials and their interactions. This could involve conditioning the denoising process on the specific material properties or textures. Training the FLDMs on datasets that include multi-material objects to learn the relationships between different textures and how they interact on the surface. Articulated Models: Incorporating information about the articulation or movement of different parts of the model. This could involve encoding information about joint angles or movement constraints in the latent representation. Modifying the denoising process in FLDMs to take into account the articulated nature of the model. This could involve conditioning the denoising process on the specific articulation parameters. Training the FLDMs on datasets that include articulated models to learn how textures vary with different articulations and movements.

How could the proposed framework be extended to handle more complex 3D geometries beyond single-textured meshes, such as multi-material objects or articulated models?

To extend the framework to handle more complex 3D geometries, such as multi-material objects or articulated models, several modifications and additions could be made. Multi-Material Objects: Introducing a mechanism to handle multiple textures or materials on the same mesh. This could involve extending the field latent representation to encode information about different materials or textures present on the surface. Adapting the denoising process in the Field Latent Diffusion Models (FLDMs) to account for multiple textures or materials and their interactions. This could involve conditioning the denoising process on the specific material properties or textures. Training the FLDMs on datasets that include multi-material objects to learn the relationships between different textures and how they interact on the surface. Articulated Models: Incorporating information about the articulation or movement of different parts of the model. This could involve encoding information about joint angles or movement constraints in the latent representation. Modifying the denoising process in FLDMs to take into account the articulated nature of the model. This could involve conditioning the denoising process on the specific articulation parameters. Training the FLDMs on datasets that include articulated models to learn how textures vary with different articulations and movements.

What are the potential limitations of the isometry-equivariant design, and how could it be relaxed to enable more diverse texture synthesis while maintaining high fidelity?

The isometry-equivariant design, while beneficial for consistent and repeatable results, may have limitations in capturing certain types of texture variations and patterns. Some potential limitations include: Limited Texture Diversity: Isometry-equivariance constrains the model to replicate textures across locally similar regions, potentially limiting the diversity of synthesized textures. Difficulty in Handling Global Transformations: Isometry-equivariance may struggle with capturing global transformations or deformations that significantly alter the texture pattern across the entire surface. Complex Texture Patterns: Capturing complex texture patterns that exhibit non-local relationships or intricate details may be challenging within an isometry-equivariant framework. To relax these limitations and enable more diverse texture synthesis while maintaining high fidelity, the isometry-equivariant design could be enhanced or modified in the following ways: Introducing Non-Equivariant Components: Incorporating non-equivariant components in the model architecture to capture global transformations or complex texture patterns that go beyond local isometries. Hybrid Approaches: Combining isometry-equivariant models with non-equivariant models to leverage the benefits of both approaches, allowing for a balance between texture diversity and fidelity. Adaptive Learning: Implementing adaptive learning mechanisms that can dynamically adjust the level of equivariance based on the texture complexity or the nature of the surface deformations.

What are the potential limitations of the isometry-equivariant design, and how could it be relaxed to enable more diverse texture synthesis while maintaining high fidelity?

The isometry-equivariant design, while beneficial for consistent and repeatable results, may have limitations in capturing certain types of texture variations and patterns. Some potential limitations include: Limited Texture Diversity: Isometry-equivariance constrains the model to replicate textures across locally similar regions, potentially limiting the diversity of synthesized textures. Difficulty in Handling Global Transformations: Isometry-equivariance may struggle with capturing global transformations or deformations that significantly alter the texture pattern across the entire surface. Complex Texture Patterns: Capturing complex texture patterns that exhibit non-local relationships or intricate details may be challenging within an isometry-equivariant framework. To relax these limitations and enable more diverse texture synthesis while maintaining high fidelity, the isometry-equivariant design could be enhanced or modified in the following ways: Introducing Non-Equivariant Components: Incorporating non-equivariant components in the model architecture to capture global transformations or complex texture patterns that go beyond local isometries. Hybrid Approaches: Combining isometry-equivariant models with non-equivariant models to leverage the benefits of both approaches, allowing for a balance between texture diversity and fidelity. Adaptive Learning: Implementing adaptive learning mechanisms that can dynamically adjust the level of equivariance based on the texture complexity or the nature of the surface deformations.

What are the potential limitations of the isometry-equivariant design, and how could it be relaxed to enable more diverse texture synthesis while maintaining high fidelity?

The isometry-equivariant design, while beneficial for consistent and repeatable results, may have limitations in capturing certain types of texture variations and patterns. Some potential limitations include: Limited Texture Diversity: Isometry-equivariance constrains the model to replicate textures across locally similar regions, potentially limiting the diversity of synthesized textures. Difficulty in Handling Global Transformations: Isometry-equivariance may struggle with capturing global transformations or deformations that significantly alter the texture pattern across the entire surface. Complex Texture Patterns: Capturing complex texture patterns that exhibit non-local relationships or intricate details may be challenging within an isometry-equivariant framework. To relax these limitations and enable more diverse texture synthesis while maintaining high fidelity, the isometry-equivariant design could be enhanced or modified in the following ways: Introducing Non-Equivariant Components: Incorporating non-equivariant components in the model architecture to capture global transformations or complex texture patterns that go beyond local isometries. Hybrid Approaches: Combining isometry-equivariant models with non-equivariant models to leverage the benefits of both approaches, allowing for a balance between texture diversity and fidelity. Adaptive Learning: Implementing adaptive learning mechanisms that can dynamically adjust the level of equivariance based on the texture complexity or the nature of the surface deformations.