Materialist: Physically Based Editing Using Single-Image Inverse Rendering
📰 ArXiv cs.AI
Learn how Materialist enables physically consistent image editing using single-image inverse rendering, overcoming limitations of neural networks and multi-view optimization
Action Steps
- Implement Materialist using PyTorch to achieve physically based editing
- Run single-image inverse rendering to estimate material properties and lighting
- Configure the neural network to initialize the physically based rendering process
- Test the edited images for physical consistency and realism
- Apply Materialist to various image editing tasks, such as relighting and material replacement
Who Needs to Know This
Computer vision engineers and researchers can benefit from this technique to improve image editing capabilities, while product managers can explore its applications in various industries
Key Insight
💡 Materialist combines the strengths of neural networks and physics-based rendering to achieve physically consistent image editing from a single image
Share This
🔍 Introducing Materialist: a neural-initialized physically based approach to image editing, enabling realistic and consistent results #computerVision #imageEditing
Key Takeaways
Learn how Materialist enables physically consistent image editing using single-image inverse rendering, overcoming limitations of neural networks and multi-view optimization
Full Article
Title: Materialist: Physically Based Editing Using Single-Image Inverse Rendering
Abstract:
arXiv:2501.03717v3 Announce Type: replace-cross Abstract: Achieving physically consistent image editing remains a significant challenge in computer vision. Existing image editing methods typically rely on neural networks, which struggle to accurately handle shadows and refractions. Conversely, physics-based inverse rendering often requires multi-view optimization, limiting its practicality in single-image scenarios. In this paper, we propose Materialist, a neural-initialized physically based ren
Abstract:
arXiv:2501.03717v3 Announce Type: replace-cross Abstract: Achieving physically consistent image editing remains a significant challenge in computer vision. Existing image editing methods typically rely on neural networks, which struggle to accurately handle shadows and refractions. Conversely, physics-based inverse rendering often requires multi-view optimization, limiting its practicality in single-image scenarios. In this paper, we propose Materialist, a neural-initialized physically based ren
DeepCamp AI