Enhancing Sketch Animation: Text-to-Video Diffusion Models with Temporal Consistency and Rigidity Constraints
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| Format: | Preprint |
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2024
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| _version_ | 1866912927149195264 |
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| author | Rai, Gaurav Sharma, Ojaswa |
| author_facet | Rai, Gaurav Sharma, Ojaswa |
| contents | Animating hand-drawn sketches using traditional tools is challenging and complex. Sketches provide a visual basis for explanations, and animating these sketches offers an experience of real-time scenarios. We propose an approach for animating a given input sketch based on a descriptive text prompt. Our method utilizes a parametric representation of the sketch's strokes. Unlike previous methods, which struggle to estimate smooth and accurate motion and often fail to preserve the sketch's topology, we leverage a pre-trained text-to-video diffusion model with SDS loss to guide the motion of the sketch's strokes. We introduce length-area (LA) regularization to ensure temporal consistency by accurately estimating the smooth displacement of control points across the frame sequence. Additionally, to preserve shape and avoid topology changes, we apply a shape-preserving As-Rigid-As-Possible (ARAP) loss to maintain sketch rigidity. Our method surpasses state-of-the-art performance in both quantitative and qualitative evaluations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_19381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Sketch Animation: Text-to-Video Diffusion Models with Temporal Consistency and Rigidity Constraints Rai, Gaurav Sharma, Ojaswa Computer Vision and Pattern Recognition Graphics Animating hand-drawn sketches using traditional tools is challenging and complex. Sketches provide a visual basis for explanations, and animating these sketches offers an experience of real-time scenarios. We propose an approach for animating a given input sketch based on a descriptive text prompt. Our method utilizes a parametric representation of the sketch's strokes. Unlike previous methods, which struggle to estimate smooth and accurate motion and often fail to preserve the sketch's topology, we leverage a pre-trained text-to-video diffusion model with SDS loss to guide the motion of the sketch's strokes. We introduce length-area (LA) regularization to ensure temporal consistency by accurately estimating the smooth displacement of control points across the frame sequence. Additionally, to preserve shape and avoid topology changes, we apply a shape-preserving As-Rigid-As-Possible (ARAP) loss to maintain sketch rigidity. Our method surpasses state-of-the-art performance in both quantitative and qualitative evaluations. |
| title | Enhancing Sketch Animation: Text-to-Video Diffusion Models with Temporal Consistency and Rigidity Constraints |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2411.19381 |