Animate Your Motion: Turning Still Images into Dynamic Videos

• URL: http://arxiv.org/abs/2403.10179v3
• Date: Tue, 16 Jul 2024 19:29:13 GMT
• Title: Animate Your Motion: Turning Still Images into Dynamic Videos
• Authors: Mingxiao Li, Bo Wan, Marie-Francine Moens, Tinne Tuytelaars,
• Abstract summary: We introduce Scene and Motion Conditional Diffusion (SMCD), a novel methodology for managing multimodal inputs. SMCD incorporates a recognized motion conditioning module and investigates various approaches to integrate scene conditions. Our design significantly enhances video quality, motion precision, and semantic coherence.
• Score: 58.63109848837741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, diffusion models have made remarkable strides in text-to-video generation, sparking a quest for enhanced control over video outputs to more accurately reflect user intentions. Traditional efforts predominantly focus on employing either semantic cues, like images or depth maps, or motion-based conditions, like moving sketches or object bounding boxes. Semantic inputs offer a rich scene context but lack detailed motion specificity; conversely, motion inputs provide precise trajectory information but miss the broader semantic narrative. For the first time, we integrate both semantic and motion cues within a diffusion model for video generation, as demonstrated in Fig 1. To this end, we introduce the Scene and Motion Conditional Diffusion (SMCD), a novel methodology for managing multimodal inputs. It incorporates a recognized motion conditioning module and investigates various approaches to integrate scene conditions, promoting synergy between different modalities. For model training, we separate the conditions for the two modalities, introducing a two-stage training pipeline. Experimental results demonstrate that our design significantly enhances video quality, motion precision, and semantic coherence.

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