Hacker News AIT2·Models
72 pts
The paper introduces a self-supervised approach to in-the-wild image relighting that combines the physical accuracy of traditional rendering with the photorealism of neural rendering. A colored mesh inferred from monocular geometry and intrinsic components lets users define 3D lighting, which is path-traced and then refined by a feed-forward neural renderer trained via a differentiable rendering process.