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Practical AI: Machine Learning, Data Science, LLM
Video generation with realistic motion
Jan 23, 2025
Paras Jain, CEO of Genmo, leads a company dedicated to creating videos with realistic motion. He discusses the current surge in video generation tools and the challenges models face, particularly in achieving lifelike walking motions. Paras shares insights on the evolution from traditional GANs to advanced diffusion models like Mochi, emphasizing the importance of quality data. He also envisions a future where AI empowers creativity in storytelling, making video creation accessible and enhancing originality in content.
45:11
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Quick takeaways
- The evolution of video generation technology reflects a shift towards more creative AI applications, emphasizing realistic motion and human-like interactions.
- Data curation presents significant challenges in video generation, requiring advanced architectures to handle the vast datasets necessary for training models effectively.
Deep dives
Emergence of Video Generation Technologies
The advent of video generation technology marks a significant milestone in artificial intelligence, reflecting a shift from traditional AI applications like language processing to more creative modalities. The evolution commenced with advancements in image generation, which set a foundation for video as a complex form of creative expression. The complexity of video generation arises not just from the sheer volume of data, but also from the need for understanding motion, physics, and realism. As highlighted by advancements in models like Sora from OpenAI, the growing capabilities in video generation are a bellwether for the future of AI technologies.
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