AI Image Completion and Video Generation - Recreating Reality

Table of Contents

• Introduction
• Image Completion Examples
• Video Generation from Still Images
• Comparison to Prior Work
• Remaining Challenges and Future Work
• Conclusion

Introduction to Image-GPT: An Incredible AI-Based Image Completion Technique

In June 2020, OpenAI published an astonishing AI method called Image-GPT. The premise was simple to grasp but enormously challenging to implement in practice: provide the AI system with an incomplete image and ask it to fill in the missing pixels. This requires a deep understanding of the visual world and tremendous artistic skill to generate realistic completions across countless image categories and styles.

So how well did Image-GPT perform at this monumentally difficult task? Let's explore some jaw-dropping examples.

Image Completion Capabilities of Image-GPT

Clearly this is a cat, but the most intriguing part has been excerpted from the image. What could that shape be? A piece of paper? Something else entirely? Let's allow the AI to complete the image and find out!

Indeed, that makes sense given the context. Now let's analyze an even more complex water droplet example. As humans familiar with fluid dynamics, we would expect to see a splash given the remnants of ripples. But does Image-GPT grasp that as well?

Remarkably, yes! The AI accurately completed the image with an outward splash. And here is the original photo for comparison. Astounding!

Cat Image Completion

The cat image demonstrates Image-GPT's capacity to logically complete images of common objects by leveraging its understanding of the visual world. Rather than hallucinating arbitrary pixels, it sensitively fills in the missing region with a folded piece of paper that aligns cleanly with the surrounding contours. This showcases the AI's contextual comprehension capabilities.

Water Droplet Completion

Meanwhile, the water droplet example highlights the method's physics intuition. It appropriately visualizes an outward ripple effect based on the prior state of the fluid and without seeing the original photo first. This is exceptionally difficult even for humans without seeing countless examples of splashing liquids. So for an AI system to plausibly simulate fluid dynamics after observing just a single static frame is hugely promising.

Video Generation Capabilities

Now, if Image-GPT can realistically complete static images of complex phenomenon like splashing fluids, an intriguing prospect presents itself - could the AI potentially not just fill in images but actually generate full videos?

Initially this sounds like sheer science fiction. But astonishingly, the researchers behind this new technique decided to push the boundaries and attempt exactly that, with eye-popping results.

Eulerian Motion Synthesis

The proposed method, called Eulerian Motion Synthesis, mimics the intuitive process humans follow when envisioning how a static image could be animated. First, we mentally pinpoint regions that seem dynamic, like smoke or liquid. Next, we estimate plausible motion trajectories, imagining how pixels might logically shift over time based on our real-world experience. Remarkably, this AI system implements those same conceptual stages. By recognizing the fluid texture and outgoing ripples, the model synthesizes a visually consistent splash animation that even loops seamlessly!

Motion Fields

The researchers also visualize the predicted pixel motion trajectories that Image-GPT generates to animate images. These "motion fields" precisely delineate which regions the AI wants to set in motion and how. Examining these motion vectors offers rare insight into the model's perception of movement within a static scene. It highlights exactly which areas exhibit visually extractable dynamics from a single frame.

Unexpected Successes

I discovered several unexpected image types that this method handles remarkably well. First, reflections generate fairly plausible motion! Second, fire animations demonstrate convincing fluid-like behavior. And now, squeeze that research paper tight, because here comes the most shocking example...my algorithmically-generated beard springs to life! Yes, you read that correctly - an AI-created portrait of my facial hair is now rippling as though submerged in water, courtesy of a state-of-the-art generative model. Of course this last one is just a cheerful coincidence rather than a rigorous result. But nonetheless, it showcases this technology's enormous potential when applied to everyday images well outside its original scope.

Comparison to Prior Work

How does Eulerian Motion Synthesis compare quantitatively to previous state-of-the-art techniques?

A 2019 method called Visual Flow Synthesis also yielded impressive animations from single frames. However, it relied more on approximating overall motion dynamics rather than deeply understanding the underlying scene structure.

In side-by-side comparisons, the new approach clearly infers more accurate and detailed movements, resulting in markedly improved video quality.

Significant Improvements

Unlike most modern AI innovations that provide modest gains over their predecessors, Eulerian Motion Synthesis represents a gigantic leap forward in multiple respects. It demonstrates unambiguously superior performance on nearly all illustration categories without the typical accuracy trade-offs characteristic of competing algorithms. Achieving across-the-board enhancement for such an immensely intricate task highlights how far artificial intelligence capabilities have advanced over just the past few years. What astounding progress we will witness in another couple research cycles!

Remaining Challenges

Understandably, some image aspects continue posing difficulties for Eulerian Motion Synthesis, presenting opportunities for future work.

Areas for Improvement

For instance, while the method excels at animating liquids, gases, and smoke, predicting solid object movement proves more troublesome. The altar region in this example should shift over time but fails to exhibit flow. Additionally, while reflections generally translate plausibly, accurately modeling refraction through transparent materials remains an open challenge. Finally, capturing the intricate dynamics of slender forms like hair or grass leaves much room for enhancement.

Conclusion

With Image-GPT, OpenAI has demolished long-standing barriers in AI-based image editing and generation.

The superb video synthesis capabilities of techniques like Eulerian Motion Synthesis were still sheer fantasy just a few years ago. And now, we can animate virtually any photo with a level of quality once only achievable via expert manual editing.

As research in this exciting domain accelerates, I eagerly anticipate seeing what novel innovations the coming years will bring. What an extraordinary time for computer vision and creativity!

FAQ

Q: How does Image-GPT work?
A: Image-GPT is an AI system that can complete missing parts of an image by filling in realistic looking pixels based on understanding the context and content of the image.

Q: What examples did Image-GPT do well on?
A: Image-GPT successfully completed images of a cat and water droplet splash in realistic and sensible ways.

Q: What is Eulerian Motion Synthesis?
A: Eulerian Motion Synthesis is a new AI technique that can generate short video clips from a single still image by predicting motion trajectories for objects within the image.

Q: What unexpected examples worked with Eulerian Motion Synthesis?
A: Surprisingly, the technique worked fairly well on animating reflections, fire, and even a procedurally generated beard.

Q: How does this new method compare to previous work?
A: This technique represents a significant leap forward in quality and capabilities compared to prior work in image and video generation.

Q: What are some limitations of the current approach?
A: It still struggles with animating some thin/complex geometry, refraction effects, and identifying all relevant areas for motion.

Q: What does the future hold for this technology?
A: With continued research, techniques like this could produce photorealistic image and video generation from limited input data.

Q: How was the video generated for the beard example?
A: An AI-generated image of a beard was animated using Eulerian Motion Synthesis, as if it were a fluid simulation.

Q: What makes this technique unique?
A: It is rare for new research methods to outperform previous techniques across the board like this, representing a true breakthrough.

Q: Why is this important for graphics and video production?
A: Techniques like this could drastically reduce manual labor for generating realistic imagery, animations, and effects in the future.