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The University of Texas at Austin
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Consistent spherical parameterizations can be learned across diverse shapes, drastically reducing geometric distortion and enhancing model robustness.
Achieving state-of-the-art mesh quality with 18x faster inference times by directly generating triangle soups while respecting crucial symmetries.
