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Found 4 papers, 0 papers with code
Training-Free Semantic Video Composition via Pre-trained Diffusion Model
Therefore, we propose a training-free pipeline employing a pre-trained diffusion model imbued with semantic prior knowledge, which can process composite videos with broader semantic disparities.
F3-Pruning: A Training-Free and Generalized Pruning Strategy towards Faster and Finer Text-to-Video Synthesis
Recently Text-to-Video (T2V) synthesis has undergone a breakthrough by training transformers or diffusion models on large-scale datasets.
Make-A-Storyboard: A General Framework for Storyboard with Disentangled and Merged Control
Story Visualization aims to generate images aligned with story prompts, reflecting the coherence of storybooks through visual consistency among characters and scenes. Whereas current approaches exclusively concentrate on characters and neglect the visual consistency among contextually correlated scenes, resulting in independent character images without inter-image coherence. To tackle this issue, we propose a new presentation form for Story Visualization called Storyboard, inspired by film-making, as illustrated in Fig. 1. Specifically, a Storyboard unfolds a story into visual representations scene by scene.
MotionZero:Exploiting Motion Priors for Zero-shot Text-to-Video Generation
To tackle the two issues, we propose a prompt-adaptive and disentangled motion control strategy coined as MotionZero, which derives motion priors from prompts of different objects by Large-Language-Models and accordingly applies motion control of different objects to corresponding regions in disentanglement.
