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Skewness

Skewness

The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...

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Related Experiment Video

Updated: May 24, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Controllable Human Video Generation From Sparse Sketches.

Linzi Qu, Jiaxiang Shang, Miu-Ling Lam

IEEE Transactions on Visualization and Computer Graphics

|March 3, 2025

Summary

This summary is machine-generated.

Sketch2HumanVideo enables sketch-based human video generation, overcoming limitations of pose-only control. This novel approach offers appearance consistency and shape variation for realistic fashion video synthesis.

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Area of Science:

Computer Vision
Artificial Intelligence
Computer Graphics

Background:

Current human fashion video generation primarily focuses on pose control.
Existing methods lack sketch-based control due to missing appearance-consistent and shape-varying data.
Sequential input requirements limit real-world applicability of current video generation techniques.

Purpose of the Study:

To introduce Sketch2HumanVideo, a novel approach for sketch-controllable human video generation.
To enable precise multi-view control of shape motion using temporally sparse sketches and pose sequences.
To generate realistic human videos with appearance consistency and shape variation.

Main Methods:

Developed a sparse sketch encoder for precise control of shape motion.
Leveraged pretrained models to synthesize a dataset with shape-varying, appearance-consistent examples.
Implemented an enlarging-and-resampling scheme to enhance high-frequency details in generated videos.

Main Results:

Sketch2HumanVideo achieves sketch-controllable human video generation with temporally sparse sketches, sparse pose sequences, and reference appearance images.
The method demonstrates superior performance and flexible control compared to state-of-the-art approaches.
Generated videos exhibit enhanced high-frequency details, improving realism.

Conclusions:

Sketch2HumanVideo successfully addresses limitations in current human video generation, enabling sketch-based control.
The approach provides a new paradigm for creating realistic and controllable fashion videos.
Future work will involve releasing the code to facilitate further research and application.