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Single-step flow-matching DiTs can surpass multi-step generative models in geometric accuracy, slashing depth error by up to 26% while recovering fine-grained details down to individual strands of hair.
Achieve state-of-the-art image and NeRF reconstruction with a parameter-efficient implicit neural representation inspired by classical subtractive synthesis.