MemCast
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#automation8#simulation3#synthetic-data1
Balancing realism with controllability is key for effective simulation
  • Purely photorealistic renderings may hide underlying physical parameters, making it hard to extract forces.
  • Conversely, overly abstract simulations lack the visual richness needed for perception training.
  • Marble’s architecture lets developers toggle fidelity levels, choosing higher‑resolution splats for perception and lower‑resolution physics‑ready meshes for dynamics.
  • This trade‑off strategy yields datasets that are both visually convincing and analytically useful.
Fei-Fei LiLatent Space00:41:30

Supporting quotes

Quote
00:41:30
“There are emergent use cases that just fall out of the model without being specifically built.” — Fei-Fei Li
Quote
00:58:31
“The AI community is moving from language models to spatial intelligence as a new frontier.” — Fei-Fei Li

From this concept

Synthetic Data for Embodied AI and Robotics

Robotics suffers from a lack of high-quality, diverse training data. Marble can synthesize realistic 3-D environments, providing a middle ground between scarce real-world recordings and uncontrolled internet video. By generating controllable scenarios, researchers can train agents that transfer to real robots more effectively.

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