Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

Resume:

1.- Generative AI can create and manipulate realistic images, including human faces, enabling things not possible in physical reality.

2.- CGI and visual effects have transformed storytelling in movies, with heavy usage in films like Avatar.

3.- Realistic CGI faces were difficult due to the uncanny valley effect - appearing creepy when close to but not fully photorealistic.

4.- Data capture and computer vision, like high-res actor scans, helped solve the uncanny valley problem but required complex systems.

5.- The speaker developed more deployable, automated real-time facial tracking systems, later used in technologies like iPhone Animoji.

6.- Facial reenactment, like recreating Paul Walker's face in Furious 7, required intensive manual labor and high costs.

7.- Deep neural networks, especially convolutional neural networks, enabled more robust computer vision for facial analysis.

8.- Generative adversarial networks (GANs) allowed generating realistic images, like fake faces, by pitting generator and discriminator networks against each other.

9.- The speaker's company developed real-time face swapping and facial reenactment using a single photo, enabling "deep fakes".

10.- Deep fakes led to nonconsensual celebrity pornography, disinformation campaigns, scams using fake identities, and other malicious uses.

11.- The accessibility of deep fake tech has made extremely convincing generated content easy for anyone to produce and spread.

12.- Deep fake detection based on biometrics or deep learning can help identify fake content. Commercial and research solutions are advancing.

13.- Raising awareness of deep fakes' potential is important. The speaker demoed real-time deep faked conversations to showcase the tech.

14.- The speaker's company uses generative AI to enhance digital avatars and for positive use cases in visual effects.

15.- Examples: Puppeteering a speaking baby in a movie, de-aging/face replacement for actors in shows like Slumberland and Fallout.

16.- AI lip-syncing foreign language films is a major use case, translating actor performances without typical bad dubbing.

17.- Pinscreen AI is a new product to let users upload videos and AI translate/lip-sync them into any language.

18.- Beyond faces, generative AI aims to generate any desired content using techniques like diffusion models.

19.- Diffusion models like Stable Diffusion break image generation into denoising steps using text prompts to guide the generation.

20.- Examples of impressive text-to-image AI include DALL-E 2, Midjourney, and Runway ML's text-to-video model.

21.- 30 years ago, the T-1000 in Terminator 2 inspired the speaker by showing CGI could create anything imaginable.

22.- Breakthroughs in realistic CGI led to films with thousands of VFX shots and only a couple fully real shots.

23.- Early face capture required expensive multi-camera rigs. The speaker worked on more practical real-time face tracking systems.

24.- One of the speaker's face tracking technologies was acquired by Apple and became the basis for Animoji.

25.- Generative AI can convincingly simulate new facial expressions from a single still photo, having many entertainment/visual effects applications.

26.- However, the same AI technologies have enabled nonconsensual deepfake pornography, fraud, political disinformation and other malicious uses.

27.- With AI-generated media now accessible to anyone, not just VFX studios, detection and public awareness are critical.

28.- The speaker's company uses AI face rendering to enhance digital avatars, de-age actors, and automatically dub foreign films.

29.- Pinscreen.ai will be a productized version of their AI dubbing tech for automated face-driven translation of any video.

30.- Beyond faces, advanced generative AI techniques like diffusion models can now generate almost any content imaginable just from text.

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