Researchers can turn a single photo into a video

Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.

This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.

An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static.

What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.

The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings.

This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind.

Internet: <www.sciencedaily.com> (adapted).

Researchers can turn a single photo into a video

Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.

This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.

An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static.

What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.

The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings.

This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind.

Internet: <www.sciencedaily.com> (adapted).

Researchers can turn a single photo into a video

Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.

This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.

An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static.

What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.

The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings.

This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind.

Internet: <www.sciencedaily.com> (adapted).

Researchers can turn a single photo into a video

Sometimes photos cannot truly capture a scene. How much more epic would that vacation photo of Niagara Falls be if the water were moving? Researchers at the University of Washington have developed a deep learning method that can do just that: if given a single photo of a waterfall, the system creates a video showing that water cascading down. All that’s missing is the roar of the water and the feeling of the spray on your face.

This method can animate any flowing material, including smoke and clouds. This technique produces a short video that loops seamlessly, giving the impression of endless movement.

An expert says that a picture captures a moment frozen in time, but a lot of information is lost in a static image. That makes people wonder what led to that moment, and how things are changing. If people think about the last time that they found themselves fixated on something really interesting, chances are, it was’t totally static.

What is special about that method is that it doesn’t require any user input or extra information. All that is needed is a picture. And it produces as output a high-resolution, seamlessly looping video that quite often looks like a real video. Developing a method that turns a single photo into a believable video has been a challenge for the field.

The system consists of two parts: first, it predicts how things were moving when the photo was taken, and then uses that information to create the animation. Then the system uses that information to determine if and how each pixel should move. Finally, the researchers want their animation to loop seamlessly to create a look of continuous movement. The animation network follows a few tricks to keep things clean, including transitioning different parts of the frame at different times and deciding how quickly or slowly to blend each pixel depending on its surroundings.

This method works best for objects with predictable fluid motion, like water, fire or smoke. These types of motions obey the same set of physical laws, and there are usually cues in the image that tell us how things should be moving. Currently, the technology struggles to predict how reflections should move or how water distorts the appearance of objects beneath it. In the future, the researchers would like to extend their work to operate on a wider range of objects, like animating a person’s hair blowing in the wind.

Internet: <www.sciencedaily.com> (adapted).