AI finally sleeps, Gemini 4 Carbon, ETH cracked, energy-based models, Anthropic IPO: AI NEWS
Useful for technical AI filmmakers and virtual production creators looking to integrate open-source 3D Gaussian splatting, real-time character animation, and action-conditioned video generation into their pipelines.
What this lesson covers
The video rounds up major weekly artificial intelligence developments spanning character animation, 3D asset creation, and controllable video synthesis. It highlights open-source releases including NVIDIA's real-time MotionBricks, the lightweight IDOL Gaussian splatting generator, SceneBloom interior modeling, and UniAction video control.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 01:14 ↗
MotionBricks generates real-time, smooth character and humanoid robot motion transitions inside Unreal Engine from target poses and movement instructions.
- 02:05 ↗
MotionBricks runs at up to 15,000 frames per second with 2ms latency using two open-source 800 MB models trained on over 350,000 motion clips.
- 03:13 ↗
IDOL creates 3D Gaussian splatting assets from single images using a 0.66B parameter model requiring only 8 GB of VRAM.
- 04:31 ↗
SceneBloom generates physically consistent 3D interior scenes from text prompts by zoning rooms, creating layouts with Flux, and placing non-overlapping 3D assets.
- 07:00 ↗
UniAction uses a unified model architecture to generate video conditioned on images, text prompts, and discrete action controls like camera paths and force application.
Workflow outlined in the video
- Download the open-source MotionBricks models from GitHub to evaluate real-time character animation in Unreal Engine.
- Set up IDOL locally on consumer hardware with 8 GB VRAM to test single-image to 3D Gaussian splatting generation.
- Use SceneBloom's released LoRA adapters and 3D asset library to produce furnished room layouts from text descriptions.
- Clone the UniAction repository to experiment with action-guided, controllable video generation using custom camera and pose trajectories.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
How to interpret the numbers
442,197 views captured 5 November 2026. ReelStack recommendation score: 57/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 1.01× current views vs the channel's recent median; same-age history is not yet available. View-evidence factor 100% (views / (views + 1,000))
- 25% freshness: 81/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 38/100; likes + 4× comments, smoothed with a 500-view neutral prior