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University of Wisconsin-Madison
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Generating missing multi-view data from diverse driving videos boosts closed-loop driving robustness in edge cases by over 30%.
MoRE achieves a staggering 44 percentage point increase in deployment success rates by seamlessly integrating behavior mode redirection into policy weights, eliminating the need for inference-time adjustments.
Current video editing AIs still struggle to balance visual quality, instruction adherence, and localized edits, as revealed by a new benchmark designed to disentangle these factors.
LLM-generated explanations often fail to help users identify incorrect answers, and simply scaling models or applying post-training doesn't fix the problem.
Generating videos from multiple views of a subject is now possible, thanks to a new method that enforces 3D consistency and avoids confusion between different subjects and views.
