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FlashTex: Fast Relightable Mesh Texturing with LightControlNet

Published 20 Feb 2024 in cs.GR, cs.CV, and cs.LG | (2402.13251v3)

Abstract: Manually creating textures for 3D meshes is time-consuming, even for expert visual content creators. We propose a fast approach for automatically texturing an input 3D mesh based on a user-provided text prompt. Importantly, our approach disentangles lighting from surface material/reflectance in the resulting texture so that the mesh can be properly relit and rendered in any lighting environment. We introduce LightControlNet, a new text-to-image model based on the ControlNet architecture, which allows the specification of the desired lighting as a conditioning image to the model. Our text-to-texture pipeline then constructs the texture in two stages. The first stage produces a sparse set of visually consistent reference views of the mesh using LightControlNet. The second stage applies a texture optimization based on Score Distillation Sampling (SDS) that works with LightControlNet to increase the texture quality while disentangling surface material from lighting. Our algorithm is significantly faster than previous text-to-texture methods, while producing high-quality and relightable textures.

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Citations (12)
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Summary

  • The paper introduces LightControlNet, an innovative illumination-aware model that separates lighting effects from material properties for dynamic relighting.
  • It employs a two-stage pipeline that achieves over 10x speed-up and improved texture quality, validated by FID, KID, and user evaluations.
  • The method enhances 3D mesh texturing for real-time applications in gaming, film, and AR/VR by automating and optimizing texture generation.

FlashTex: Enhancing 3D Mesh Texturing with LightControlNet for Fast, High-Quality, and Relightable Outputs

Introduction to Texturing Challenges in 3D Meshes

Creating detailed textures for 3D meshes is crucial for a myriad of industries, from gaming and film to AR/VR applications. Traditional methods of generating these textures have long been labor-intensive and slow, often resulting in static textures that do not dynamically respond to changes in environmental lighting. To address these challenges, researchers have turned towards leveraging text-to-image diffusion models, promising faster generation times and higher quality outputs. Despite this progress, existing methods often produce textures with baked-in lighting conditions that limit their adaptability to new lighting environments, alongside issues like slow generation speeds and visual artifacts. The proposed method, FlashTex, introduces an innovative approach to overcome these drawbacks, offering a significant advancement in automated mesh texturing.

Novel Contributions: LightControlNet and the Two-Stage Pipeline

FlashTex's core contribution is the development of LightControlNet, an illumination-aware text-to-image model built upon the ControlNet architecture. This model is capable of generating textures for 3D meshes that can be accurately relit in different lighting scenarios by disentangling lighting effects from surface material properties. The text-to-texture generation process is divided into two stages:

  1. Multi-view Visual Prompting: Utilizing LightControlNet, this stage produces a sparse set of reference views with consistent visual appearance across multiple viewpoints. This approach ensures style consistency and mitigates multi-view inconsistency issues typically encountered in texture generation.
  2. Texture Optimization with SDS: Building upon the reference views generated in the first stage, this phase employs Score Distillation Sampling (SDS) enhanced with LightControlNet guidance. This novel texture optimization technique adeptly increases texture quality while effectively separating lighting from material/reflectance properties.

This two-stage process not only accelerates texture generation—achieving more than a 10x speed-up compared to previous SDS-based methods—but also significantly improves the quality of the textures produced, as substantiated by quantitative metrics (FID, KID) and user evaluations.

Theoretical and Practical Implications

The introduction of FlashTex marks a pivotal advancement in automatic mesh texturing techniques, with profound theoretical and practical implications:

  • Efficiency and Quality: FlashTex underscores the possibility of marrying efficiency with quality in texture generation, a critical aspect for real-time applications in gaming and interactive media.
  • Dynamic Relighting: By disentangling lighting from surface material properties, FlashTex facilitates the creation of textures that can be dynamically relit, enhancing realism and immersion in digital content.
  • Future AI Developments: The method sets a new benchmark for text-to-texture generation, potentially guiding future research into more complex scenarios, such as generating textures for amorphous or highly intricate objects.

Evaluations and Future Directions

Evaluation on the Objaverse dataset shows FlashTex's superiority over existing text-to-texture methods, both in terms of texture quality and the ability to dynamically relight textures in varied lighting conditions. Despite its achievements, FlashTex does have limitations, including occasional baked-in lighting artifacts and challenges in material property disentanglement in textures. Thus, future work could explore more sophisticated models for better generalization across diverse mesh types and the development of even more efficient and accurate text-to-texture conversion processes, further refining the dynamic relighting capabilities.

Conclusion

FlashTex represents a significant stride forward in the automation of 3D mesh texturing, offering improvements in speed, quality, and dynamic relighting capabilities. By introducing LightControlNet and a novel two-stage text-to-texture pipeline, this work not only addresses existing limitations but also opens up new avenues for research and application in the field of 3D content creation.

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