This AI Creates Videos From a Couple of Images

Believe it or not, what you see is actually not a video.

It was made from a simple collection of photos and transformed into a 3-dimensional model! The best thing is that it didn’t even need a thousand pictures, only a few, and creates the missing information afterward!

As you can see, the results are amazing, but they aren’t easy to generate and require a bit more than only the images as inputs. Let’s dive in and see how the researchers achieved this as well as more fantastic examples...

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believe it or not what you see here is

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actually not a video it was made from a

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simple collection of photos and

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transformed into a three-dimensional

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model the best thing is that it didn't

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even need a thousand pictures only a few

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and could create the missing information

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afterward as you can see the results are

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amazing but they aren't easy to generate

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and requires a bit more than only the

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images as inputs let's rewind a little

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imagine you want to generate a 3d model

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out of a bunch of pictures you took like

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these ones

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instead of only using these pictures you

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will also need to feed it a point cloud

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a point cloud is basically the simplest

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form of a 3d model you can see it as a

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draft version of your 3d model

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represented by sparse points in 3d space

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that looks just like this these points

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also have the appropriate colors and

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luminance from the images you took a

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point cloud is made using multiple

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photos triangulating the corresponding

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points to understand their position in

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3d space you now have your photos and a

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point cloud or as we said your 3d draft

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you are ready to improve it by the way

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if you find this interesting i invite

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you to subscribe like the video and

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share the knowledge by sending this

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video to a friend i'm sure they will

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love it and they will be grateful to

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learn something new because of you and

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if you don't no worries thank you for

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watching first you will take your images

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and point cloud and send it to the first

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module the rasterizer remember the point

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cloud is basically our initial 3d

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reconstruction or our first draft the

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rasterizer will produce the first low

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quality version of our 3d image using

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the camera parameters from your pictures

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and the point cloud it will basically

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try to fill in the holes in your initial

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point cloud representation approximating

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colors and understanding depth this is a

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very challenging task as it has to both

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understand the images that do not cover

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all the angles and the sparse point

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cloud 3d representation it might not be

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able to fill in the whole 3d image

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intelligently due to this lack of

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information which is why it looks like

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this the still unknown pixels are

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replaced by the background and this is

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all still very low resolution containing

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many artifacts since it's far from

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perfect this step is made on multiple

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resolutions to help the next module with

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more information

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the second module is the neural renderer

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this neural renderer is just a unit like

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we covered numerous times on my channel

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to take an image as input and generate a

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new version of it as output it will take

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the incomplete renderings of various

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resolutions as images understand them

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and produce a new version of each image

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in higher definition filling the holes

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this will create high resolution images

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for all missing viewpoints of the scene

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of course when i say to understand them

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it means that the two modules are

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trained together to achieve this this

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neural renderer will produce hdr novel

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images of the rendering or high dynamic

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range images which are basically more

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realistic high resolution images of the

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3d scene with better lighting the hdr

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results basically look like images of

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the scene in the real world this is

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because the hdr images will have a much

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broader range of brightness than

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traditional jpeg encoded images where

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the brightness can only be encoded on 8

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bit with a 255 to 1 range so it won't

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look great if encoded in a similar

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format a third and final module the tone

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mapper is introduced to take this

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broader range and learn an intelligent

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transformation to fit the 8-bit encoding

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better this third module aims to take

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these hdr novel images and transform

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them into ldr images covering the whole

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scene our final outputs the ldr images

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or low dynamic range images will look

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much better with traditional image

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encodings this module basically learns

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to mimic digital cameras physical lens

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and sensor properties to produce similar

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outputs from our previous real-world

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like images there are basically four

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steps in this algorithm create a point

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cloud from your images to have a first

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3d rendering of the scene fill in the

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missing holes of this first rendering as

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best as possible using the images and

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camera information and do this with

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various image resolutions use these

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various image resolutions of the 3d

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rendering in a unit to create a high

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quality hdr image of this rendering for

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any viewpoint transform the hdr images

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into ldr images for better visualization

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and voila we have the amazing looking

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video of the scene we saw at the

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beginning of the video as i mentioned

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there are some limitations one of which

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is the fact that they are highly

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dependent on the quality of the point

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cloud given for obvious reasons also if

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the camera is very close to an object or

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the point cloud 2 sparse it may cause

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holes like this one in the final

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rendering still the results are pretty

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incredible considering the complexity of

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the task we've made immense progress in

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the past year you can take a look at the

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videos i made covering other neural

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rendering techniques less than a year

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ago and compared the quality of the

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results it's pretty crazy of course this

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is just an overview of this new paper

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attacking this super interesting task in

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a novel way i invite you to read their

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excellent paper for more technical

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detail about their implementation and

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check their github repository with

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pre-trained models both are linked in

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the description below thank you very

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much for watching the whole video please

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take a second to let me know what you

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think of the overall quality of the

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videos if you saw any improvements

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recently or not and i will see you next

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week

►Read the full article: https://www.louisbouchard.ai/ai-synthesizes-smooth-videos-from-a-couple-of-images/
►Rückert, D., Franke, L. and Stamminger, M., 2021. ADOP: Approximate Differentiable One-Pixel Point Rendering, https://arxiv.org/pdf/2110.06635.pdf.
►Code: https://github.com/darglein/ADOP.
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/.