Excited to introduce our new paper, Generative Omnimatte: Learning to Decompose Video into Layers, with the amazing team at Google DeepMind!
Our method decomposes a video into complete layers, including objects and their associated effects (e.g., shadows, reflections). pic.twitter.com/pLVx1aJvca
this is the epic story of a group of talented digital artists trying to overcame daily technical challenges to achieve incredibly photorealistic projects of monsters and aliens
The law protects new works from unauthorized copying while allowing artists free rein on older works.
The Copyright Act of 1909 used to govern copyrights. Under that law, a creator had a copyright on his creation for 28 years from “publication,” which could then be renewed for another 28 years. Thus, after 56 years, a work would enter the public domain.
However, the Congress passed the Copyright Act of 1976, extending copyright protection for works made for hire to 75 years from publication.
Then again, in 1998, Congress passed the Sonny Bono Copyright Term Extension Act (derided as the “Mickey Mouse Protection Act” by some observers due to the Walt Disney Company’s intensive lobbying efforts), which added another twenty years to the term of copyright.
it is because Snow White was in the public domain that it was chosen to be Disney’s first animated feature.
Ironically, much of Disney’s legislative lobbying over the last several decades has been focused on preventing this same opportunity to other artists and filmmakers.
The battle in the coming years will be to prevent further extensions to copyright law that benefit corporations at the expense of creators and society as a whole.
Tired of having iTunes messing up your mp3 library? … Time to try MiniTunes!
– Arrange your library by Genre, Artists or Albums. – Change UI colors at will. – Edit tags and create playlists. – Consolidate your library once for all. – Windows 64 only
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
