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University of Illinois Urbana-Champaign, Virginia Tech
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GraphVid achieves superior video quality and controllability with significantly less training data, revolutionizing how we can interact with multi-object video generation.
Forget GAN inversions – now you can steer diffusion models with a dynamically weighted soup of differentiable rewards, including a VQA-based reward for language-vision reasoning, and get SOTA image edits.
