Google's Gemini 1.5 Pro can now generate videos from text prompts, offering a range of stylistic options and control over animation, transitions, and characters. This capability, available through the AI platform "Whisk," is designed for anyone from everyday users to professional video creators. It enables users to create everything from short animated clips to longer-form video content with customized audio, and even combine generated segments with uploaded footage. This launch represents a significant advancement in generative AI, making video creation more accessible and empowering users to quickly bring their creative visions to life.
Step-Video-T2V explores the emerging field of video foundation models, specifically focusing on text-to-video generation. The paper introduces a novel "step-by-step" paradigm where video generation is decomposed into discrete, controllable steps. This approach allows for finer-grained control over the generation process, addressing challenges like temporal consistency and complex motion representation. The authors discuss the practical implementation of this paradigm, including model architectures, training strategies, and evaluation metrics. Furthermore, they highlight existing limitations and outline future research directions for video foundation models, emphasizing the potential for advancements in areas such as long-form video generation, interactive video editing, and personalized video creation.
Several Hacker News commenters express skepticism about the claimed novelty of the "Step-Video-T2V" model. They point out that the core idea of using diffusion models for video generation is not new, and question whether the proposed "step-wise" approach offers significant advantages over existing techniques. Some also criticize the paper's evaluation metrics, arguing that they don't adequately demonstrate the model's real-world performance. A few users discuss the potential applications of such models, including video editing and content creation, but also raise concerns about the computational resources required for training and inference. Overall, the comments reflect a cautious optimism tempered by a desire for more rigorous evaluation and comparison to existing work.
Summary of Comments ( 123 )
https://news.ycombinator.com/item?id=43695592
Hacker News users discussed Google's new video generation features in Gemini and Whisk, with several expressing skepticism about the demonstrated quality. Some commenters pointed out perceived flaws and artifacts in the example videos, like unnatural movements and inconsistencies. Others questioned the practicality and real-world applications, highlighting the potential for misuse and the generation of unrealistic or misleading content. A few users were more positive, acknowledging the rapid advancements in AI video generation and anticipating future improvements. The overall sentiment leaned towards cautious interest, with many waiting to see more robust and convincing examples before fully embracing the technology.
The Hacker News post "Generate videos in Gemini and Whisk with Veo 2," linking to a Google blog post about video generation using Gemini and Whisk, has generated a modest number of comments, primarily focused on skepticism and comparisons to existing technology.
Several commenters express doubt about the actual capabilities of the demonstrated video generation. One commenter highlights the highly curated and controlled nature of the examples shown, suggesting that the technology might not be as robust or generalizable as implied. They question whether the model can handle more complex or unpredictable scenarios beyond the carefully chosen demos. This skepticism is echoed by another commenter who points out the limited length and simplicity of the generated videos, implying that creating longer, more narratively complex content might be beyond the current capabilities.
Comparisons to existing solutions are also prevalent. RunwayML is mentioned multiple times, with commenters suggesting that its video generation capabilities are already more advanced and readily available. One commenter questions the value proposition of Google's offering, given the existing competitive landscape. Another comment points to the impressive progress being made in open-source video generation models, further challenging the perceived novelty of Google's announcement.
There's a thread discussing the potential applications and implications of this technology, with one commenter expressing concern about the potential for misuse in generating deepfakes and other misleading content. This raises ethical considerations about the responsible development and deployment of such powerful generative models.
Finally, some comments focus on technical aspects. One commenter questions the use of the term "AI" and suggests "ML" (machine learning) would be more appropriate. Another discusses the challenges of evaluating generative models and the need for more rigorous metrics beyond subjective visual assessment. There is also speculation about the underlying architecture and training data used by Google's model, but no definitive information is provided in the comments.
While there's no single overwhelmingly compelling comment, the collective sentiment reflects cautious interest mixed with skepticism, highlighting the need for more concrete evidence and real-world applications to fully assess the impact of Google's new video generation technology.