Texture Fields: Learning Texture Representations in Function Space (1905.07259v1)

Abstract: In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models.

• The paper introduces Texture Fields, a novel framework that decouples texture from shape by modeling textures as continuous 3D functions.
• The paper demonstrates superior performance over voxel-based methods, achieving higher fidelity texture reconstruction as evidenced by improved metrics like FID and SSIM.
• The paper integrates generative models to synthesize unseen textures and highlights future research in multi-modal learning and hyper-realistic rendering.

Overview of "Texture Fields: Learning Texture Representations in Function Space"

The paper "Texture Fields: Learning Texture Representations in Function Space" offers a substantial contribution to computer vision by addressing the less-explored domain of efficient texture reconstruction for 3D objects. With existing methods restricted by low-resolution outputs or requiring specific shape parameterizations, the proposed method, Texture Fields, introduces an innovative approach by utilizing continuous 3D function space representations to overcome these limitations.

Texture Fields are established as neural networks parameterizing a continuous function in a 3D space designed to predict color values for any given 3D point. This approach separates the texture representation from the shape representation, which not only enables the reconstruction of high-resolution textures but also facilitates the integration of modern deep learning techniques. The separation allows for flexibility in using various shape representations such as voxels, point clouds, and meshes, without being constrained by their typical limitations.

Experimentally, the paper demonstrates that Texture Fields outperform existing methods in high-frequency texture representation from single images. Combining Texture Fields with state-of-the-art shape reconstruction advancements results in improving the holistic reconstruction of both textures and shapes from single inputs. Furthermore, the application of probabilistic models within this framework allows for the generation and synthesis of textures for unseen models in a generative setting.

Key Elements and Contributions

• Representation: Texture Fields represent textures as a 3D continuous function, significantly improving upon discretized representations such as those found in voxel-based methods which suffer cubic scaling with resolution.
• Integration and Independence: This method encapsulates texture information independently from shape representations, enhancing its utility across varied object types and categories without relying on explicit UV mappings or known topologies.
• Experimental Results: The proposed method showcases superior performance compared with baseline models in terms of various metrics including Fréchet Inception Distance (FID), SSIM, and Feature-$\ell_1$, underscoring its capability in realistic synthetic texture generation.
• Probabilistic Generative Models: By incorporating GANs and VAEs into Texture Fields, the authors demonstrate the method's applicability in non-supervised contexts, generating diverse textural variations that extend traditional texture synthesis capabilities.

Implications and Future Work

The implications of this work are profound for the domains of 3D model generation and computer graphics. By decoupling texture representation from the geometric topology, this model lays the groundwork for more flexible texturing solutions across various fields including augmented reality, game design, and simulation. The introduced methodology not only enhances the fidelity of texture details but also contributes to reducing computational overhead associated with volumetric representations.

Looking forward, exploration into further optimization of the predictive accuracy of Texture Fields could be a promising avenue. Delving into multi-modal learning could also enhance the system's robustness against varied inputs, spanning real-world image conditions and additional sensor information. Moreover, the amalgamation of hyper-realistic texture synthesis with precise geometry recognition is an area ripe for exploration, fueled by the foundational qualities exhibited by Texture Fields.

In conclusion, Texture Fields pioneer a nuanced approach to high-quality texture representation, serving as an instrumental development in advancing the capabilities of 3D generative models. This work represents a pivotal step forward in using continuous fields in shaders, offering potential applications that extend beyond the current paradigms in rendering and visualization technologies.