Impressive Video Data Compression

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SUMMARY

The discussion centers on advancements in video data compression techniques, particularly those outlined in the papers "One-Shot Free-View Neural Talking Head Synthesis for Video Conferencing" and "face-vid2vid." These methods significantly reduce bandwidth requirements for streaming video while also raising concerns about the potential for misuse in creating deep fakes. The compression techniques leverage actor recordings and facial point mapping to enhance video rendering, drawing parallels to traditional video compression methods using i-frames and p-frames.

PREREQUISITES
  • Understanding of video compression techniques, specifically i-frames and p-frames.
  • Familiarity with neural networks and AI applications in video processing.
  • Knowledge of facial recognition and mapping technologies.
  • Basic comprehension of streaming protocols and bandwidth management.
NEXT STEPS
  • Research "face-vid2vid" for practical applications in video conferencing.
  • Explore the implications of AI in video production and deep fake technology.
  • Study the principles of video compression, focusing on i-frames and p-frames.
  • Investigate ethical considerations surrounding AI-generated content in media.
USEFUL FOR

This discussion is beneficial for video engineers, AI researchers, content creators, and anyone involved in video streaming and compression technologies.

anorlunda
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https://arxiv.org/pdf/2011.15126.pdf
https://nvlabs.github.io/face-vid2vid/
https://wandb.ai/ayush-thakur/face-...hesis-for-Video-Conferencing--Vmlldzo1MzU4ODc

One thing in this modern world seems to be ubiquitous; the demand for streaming more and more video. The
data compression in these papers, appears to be a significant step forward in reducing the bandwidth required.

I don't know whether to call it an algorithm or an AI. The difference is blurry.

On the dark side, it also appears to enable much simpler production of deep fakes.

 
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Interesting compression. Thanks for sharing.

This seems very similar to the recent animation of old photos using the old photo and a digitized actor doing the actions to be the guide for the video rendering.

They record an actor and key facial points for each frame and then map the old photo facial points to the actors facial points to render the scene.

I imagine too the artifacts can be ignored as side-effects of the video transmission in the viewers mind.

Standard video does something simpler with i-frames and p-frames where the i-frame is a full frame of the image (like jpg or bmp) and the p-frame is what changed.

https://en.wikipedia.org/wiki/Video_compression_picture_types
 

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