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GARFIELD enables real-time, uncertainty-aware motion planning by efficiently modeling the distribution of possible scene futures, outperforming traditional methods by a factor of 97 in trajectory sampling speed.
State-of-the-art vision-language models fail to leverage visual context, leading to biased outputs, but a new training framework shows they can learn to infer concepts from image sets effectively.
By intelligently focusing compute on the most challenging image regions, Patch Forcing significantly boosts image generation quality without adding computational overhead.
Forget generating entire videos – this method distills motion into a highly compressed latent space, letting you steer scene dynamics with text prompts at unprecedented speeds.
