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Gradually Varying Flow01:29

Gradually Varying Flow

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Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
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Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
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Rapidly Varying Flow01:24

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Computer-Generated Animal Model Stimuli
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Vidsketch: Hand-drawn sketch-driven video generation with diffusion control.

Lifan Jiang1, Shuang Chen1, Boxi Wu1

  • 1State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 16, 2025
PubMed
Summary
This summary is machine-generated.

VidSketch generates high-quality video animations from hand-drawn sketches and text prompts. This novel approach simplifies video creation for users of all skill levels, overcoming limitations of previous static image and complex edge-map methods.

Keywords:
Level-Based sketch control strategySketch interpolation strategyVBenchVideo animation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Generative Models

Background:

  • Existing generative AI methods excel at static image generation from sketches.
  • Current sketch-guided video generation is limited, often requiring advanced drawing skills via edge maps.
  • There's a need for accessible tools enabling video animation synthesis from simple sketches.

Purpose of the Study:

  • To introduce VidSketch, a pioneering method for generating high-quality video animations from hand-drawn sketches and text prompts.
  • To lower the barrier to entry for video content creation, bridging the gap between amateur and professional users.
  • To provide a flexible and user-friendly approach to sketch-guided video animation.

Main Methods:

  • Developed a Sketch Interpolation Strategy to expand user-provided sketch sequences into full video frames.
  • Introduced a Level-Based Sketch Control Strategy to dynamically adjust sketch guidance based on user proficiency.
  • Integrated text prompts for enhanced control over video content generation.

Main Results:

  • VidSketch demonstrates superior performance and generalizability compared to baseline methods on VBench metrics.
  • The method successfully generates high-quality video animations from diverse sketch inputs.
  • Experimental results validate the effectiveness of the proposed interpolation and control strategies.

Conclusions:

  • VidSketch represents a significant advancement in sketch-guided video animation synthesis.
  • The approach enhances input flexibility and accommodates varying user drawing skills.
  • VidSketch empowers a wider range of users to create video content effectively.