Make 3D material look real, easy
Paper: Material Anything: Generating Materials for Any 3D Object via Diffusion (17 Pages)
Researchers from Northwestern Polytechnical University, Shanghai AI Lab, S-Lab, Nanyang Technological University are interested in creating high-quality physically-based rendering (PBR) materials for 3D objects is crucial for realism in applications such as video games and virtual reality.
Hmm..What’s the background?
Existing methods for automatically generating 3D materials are often complex, lack scalability, and struggle to handle diverse lighting conditions. Some methods require specific optimizations for each case, while others rely on multiple models, leading to potential instability and limited generalization.
The Material Anything framework is designed to be fully automated, stable, and universally applicable to various 3D objects under different lighting and texture conditions. It effectively handles texture-less objects, albedo-only objects, scanned objects, and generated objects, all within a unified framework.
So what is proposed in the research paper?
The research paper incorporates several key insights:
Material Anything uses a two-stage pipeline: image-space material generation and UV-space material refinement. This pipeline allows for the creation of high-quality, consistent, and user-friendly UV maps
Introduces a novel triple-head architecture and rendering loss to improve the stability and quality of material generation. This approach effectively bridges the gap between natural images and material maps, enabling the use of pre-trained image diffusion models for material estimation
A UV-space diffusion model refines the generated material maps, addressing issues like seams and texture holes. This refinement process, guided by a canonical coordinate map, ensures seamless and complete material representations, enhancing the overall quality of the generated materials
Outperforms existing methods in generating high-quality PBR materials for a wide range of 3D objects and lighting conditions. This is demonstrated through extensive experiments and comparisons with existing methods, including both quantitative and qualitative evaluations
What’s next?
While the model exhibits limitations in handling certain textures and removing artifacts, its overall performance demonstrates its potential to revolutionize the creation of realistic 3D content. Future work can focus on addressing these limitations and further improving the model's capabilities
Make 3D material look real, easy
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