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Achieve competitive novel-view synthesis with a 4MB footprint and 78ms inference by learning a compact global scene representation *before* decoding 3D geometry.
Generating robotic manipulation data that respects object affordances is now possible at scale, but current imitation learning methods still struggle with tasks like pouring and hanging, revealing a critical gap.