My PhD project was on example-based water animation. In my work the examples used are videos of real natural water scenes. The work aims to provide a practical alternative to fluid simulation based water animation.
Abstract
We introduce a video-based approach for producing water surface models. Recent advances in this field output high-quality results but require dedicated capturing devices and only work in limited conditions. In contrast, our method achieves a good tradeoff between the visual quality and the production cost: It automatically produces a visually plausible animation using a single viewpoint video as the input. Our approach is based on two discoveries: first, shape from shading (SFS) is adequate to capture the appearance and dynamic behavior of the example water; second, shallow water model can be used to estimate a velocity field that produces complex surface dynamics. We will provide qualitative evaluation of our method and demonstrate its good performance across a wide range of scenes.
Abstract
This paper describes an approach for automatically producing convincing water surfaces from video data in real time. Fluids simulation has long been studied in the Computer Graphics literature, but the methods developed are expensive and require input from highly trained artists. In contrast our method is a low cost Computer Vision based solution which requires only a single video as a source. Our output consists of an animated mesh of the water surface captured together with surface velocities and texture maps from the video data. As an example of what can be done with this data, a modified form of video textures is used to create naturalistic infinite transition loops of the captured water surface. We demonstrate our approach over a wide range of inputs, including quiescent lakes, breaking sea waves, and waterfalls. All source video we use are taken from a third-party publicly available database.
Abstract
This paper introduces a method for reconstructing water from real video footage. Using a single input video, the proposed method produces a more informative reconstruction from a wider range of possible scenes than the current state of the art. The key is the combination of vision algorithms and physics laws. Shape from shading is used to capture the change of the water’s surface, from which a vertical velocity gradient field is calculated. Such a gradient field is used to constrain the tracking of horizontal velocities by minimizing an energy function as a weighted combination of mass-conservation and intensity-conservation. Hence the final reconstruction contains a dense velocity field that is incompressible in 3D. The proposed method is efficient and performs consistently well across water of different types.