Research Paper#Computer Vision, Diffusion Models, Transparent Object Perception🔬 ResearchAnalyzed: Jan 3, 2026 17:00
Diffusion Models for Transparent Object Perception
Published:Dec 29, 2025 18:59
•1 min read
•ArXiv
Analysis
This paper introduces a novel approach to depth and normal estimation for transparent objects, a notoriously difficult problem for computer vision. The authors leverage the generative capabilities of video diffusion models, which implicitly understand the physics of light interaction with transparent materials. They create a synthetic dataset (TransPhy3D) to train a video-to-video translator, achieving state-of-the-art results on several benchmarks. The work is significant because it demonstrates the potential of repurposing generative models for challenging perception tasks and offers a practical solution for real-world applications like robotic grasping.
Key Takeaways
- •Proposes a novel method for depth and normal estimation of transparent objects using video diffusion models.
- •Introduces a synthetic dataset (TransPhy3D) for training the model.
- •Achieves state-of-the-art results on several benchmarks, including real-world datasets.
- •Demonstrates the potential of repurposing generative models for perception tasks.
- •Provides a practical solution for applications like robotic grasping.
Reference
“"Diffusion knows transparency." Generative video priors can be repurposed, efficiently and label-free, into robust, temporally coherent perception for challenging real-world manipulation.”