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Stop trading off fidelity for visual quality in super-resolution: a new network learns to directly optimize for human-preferred aesthetics.
By explicitly disentangling BEV features into semantic subspaces and assigning them to specialized experts, SEF-MAP achieves state-of-the-art HD map prediction, even when sensor data is degraded.
Humanoid robots can now learn complex, terrain-aware motions directly from video using a low-cost pipeline, eliminating the need for expensive MoCap data and manual motion design.
