BREAKING NEWS
LATEST POSTS
-
AI and the Law – Netflix : Using Generative AI in Content Production
https://www.cartoonbrew.com/business/netflix-generative-ai-use-guidelines-253300.html
- Temporary Use: AI-generated material can be used for ideation, visualization, and exploration—but is currently considered temporary and not part of final deliverables.
- Ownership & Rights: All outputs must be carefully reviewed to ensure rights, copyright, and usage are properly cleared before integrating into production.
- Transparency: Productions are expected to document and disclose how generative AI is used.
- Human Oversight: AI tools are meant to support creative teams, not replace them—final decision-making rests with human creators.
- Security & Compliance: Any use of AI tools must align with Netflix’s security protocols and protect confidential production material.
-
SkyworkAI Matrix-3D – Omnidirectional Explorable 3D World Generation
https://github.com/SkyworkAI/Matrix-3D
Matrix-3D utilizes panoramic representation for wide-coverage omnidirectional explorable 3D world generation that combines conditional video generation and panoramic 3D reconstruction.
- Large-Scale Scene Generation : Compared to existing scene generation approaches, Matrix-3D supports the generation of broader, more expansive scenes that allow for complete 360-degree free exploration.
- High Controllability : Matrix-3D supports both text and image inputs, with customizable trajectories and infinite extensibility.
- Strong Generalization Capability : Built upon self-developed 3D data and video model priors, Matrix-3D enables the generation of diverse and high-quality 3D scenes.
- Speed-Quality Balance: Two types of panoramic 3D reconstruction methods are proposed to achieve rapid and detailed 3D reconstruction respectively.
-
Zibra.AI – Real-Time Volumetric Effects in Virtual Production. Now free for Indies!
A New Era for Volumetrics
For a long time, volumetric visual effects were viable only in high-end offline VFX workflows. Large data footprints and poor real-time rendering performance limited their use: most teams simply avoided volumetrics altogether. It’s similar to the early days of online video: limited computational power and low network bandwidth made video content hard to share or stream. Today, of course, we can’t imagine the internet without it, and we believe volumetrics are on a similar path.
With advanced data compression and real-time, GPU-driven decompression, anyone can now bring CGI-class visual effects into Unreal Engine.
From now on, it’s completely free for individual creators!
What it means for you?
(more…)
FEATURED POSTS
-
AI Data Laundering: How Academic and Nonprofit Researchers Shield Tech Companies from Accountability
“Simon Willison created a Datasette browser to explore WebVid-10M, one of the two datasets used to train the video generation model, and quickly learned that all 10.7 million video clips were scraped from Shutterstock, watermarks and all.”
“In addition to the Shutterstock clips, Meta also used 10 million video clips from this 100M video dataset from Microsoft Research Asia. It’s not mentioned on their GitHub, but if you dig into the paper, you learn that every clip came from over 3 million YouTube videos.”
“It’s become standard practice for technology companies working with AI to commercially use datasets and models collected and trained by non-commercial research entities like universities or non-profits.”
“Like with the artists, photographers, and other creators found in the 2.3 billion images that trained Stable Diffusion, I can’t help but wonder how the creators of those 3 million YouTube videos feel about Meta using their work to train their new model.”
