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.

翻译：使用传统工具为手绘草图制作动画具有挑战性且过程复杂。草图提供了视觉化的解释基础，而将其动画化则能呈现实时场景的体验。我们提出了一种方法，能够基于描述性文本提示对给定的输入草图进行动画处理。我们的方法利用了草图笔画的参数化表示。与以往方法不同——它们难以估计平滑且准确的运动，且常常无法保持草图的拓扑结构——我们利用预训练的文本到视频扩散模型，结合SDS损失来引导草图笔画的运动。我们引入了长度-面积（LA）正则化，通过准确估计控制点在帧序列中的平滑位移来确保时序一致性。此外，为保持形状并避免拓扑变化，我们应用了保持形状的尽可能刚性（ARAP）损失来维持草图的刚性。我们的方法在定量和定性评估中均超越了现有技术水平。