Vidu 1.5: Contextual Memory Ushers in an Explosion ofVideo Generation Capabilities
A Chinese video generation model, Vidu, is leveragingLarge Language Model (LLM) techniques to achieve unprecedented levels of control and coherence in video synthesis, marking a significant leap forward in AI-driven video creation.
Recently, on platform X, the domestically developed video generation model Vidu began accepting online orders with a simple request: Hey, I have three pictures here; can you create a video from them? The response has been astonishing. Given just three images, Vidu generates seamless videos with natural interactions between people, objects, and backgrounds. Facial features and dynamic expressions remain consistent even duringsignificant movements, a feat previously challenging for video generation models.
This breakthrough comes from Vidu, a video model independently developed by Shengshu Technology, a company with strong ties to Tsinghua University. Launched in July, Vidu isnow in its 1.5 iteration, representing one of the earliest global competitors to Meta’s Sora. The most significant enhancement in Vidu 1.5 is its mastery of multi-subject consistency. This allows the model to naturally integrate multiple subjects from different reference images into a single video. Thiscapability opens up a world of creative possibilities. Imagine: Elon Musk endorsing an electric car in a traditional Chinese floral jacket without ever leaving his office, or Leonardo DiCaprio effortlessly showcasing haute couture on a virtual runway. These scenarios, once confined to the realm of fantasy, are now within reach thanks to Vidu 1.5’s enhanced capabilities. Simple prompts like a man in a floral jacket riding an electric scooter in an amusement park or a man in a red dress walking a runway are all that’s needed to generate highly realistic videos.
While the playful applications of Vidu 1.5 areimmediately apparent, the underlying technological advancements are even more impressive. This upgrade represents three major breakthroughs:
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Multi-Subject Control: Vidu 1.5 can now seamlessly manage and coordinate multiple subjects within a single video, significantly expanding the complexity and realism of generated content.
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ContextualMemory: Borrowing a key technique from LLMs, Vidu 1.5 incorporates contextual memory. This allows the model to retain and utilize information from previous frames or input images, resulting in greater coherence and consistency throughout the generated video. This is a crucial step towards more sophisticated and nuanced video generation.
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Emergent Capabilities: The combination of multi-subject control and contextual memory has led to emergent capabilities, meaning the model is exhibiting behaviors and functionalities not explicitly programmed. This suggests a significant leap in the model’s understanding and manipulation of visual information.
The development of Vidu 1.5 representsa substantial advancement in the field of AI-driven video generation. Its ability to seamlessly integrate multiple subjects and leverage contextual memory opens exciting new avenues for content creation, entertainment, and various other applications. Further research into these emergent capabilities promises even more groundbreaking advancements in the near future.
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