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What would an insider’s perspective on a video produced by an artificial intelligence system entail? You might assume that the method resembles stop-motion animation, where numerous images are crafted and combined, but that’s not entirely accurate for “diffusion models” such as OpenAl’s SORA and Google’s VEO 2.
Rather than generating a video frame-by-frame (or “autoregressively”), these technologies analyze the full sequence simultaneously. The resulting footage is frequently photorealistic; however, the method is gradual and does not permit on-the-spot adjustments.
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Adobe Research have recently created a hybrid strategy known as “CausVid” to generate videos in mere seconds. Similar to a sharp student absorbing knowledge from an experienced instructor, a comprehensive-sequence diffusion model trains an autoregressive system to quickly anticipate the next frame while maintaining high quality and coherence. CausVid’s student model subsequently generates clips from a basic text prompt, transforming a static image into a dynamic scene, prolonging a video, or modifying its outputs with new instructions mid-generation.
This versatile tool facilitates rapid, interactive content creation, condensing a 50-step task into only a few actions. It can produce various imaginative and artistic scenes, such as a paper airplane transforming into a swan, woolly mammoths traversing snow, or a child leaping into a puddle. Users can initiate a prompt like “create an image of a man crossing the street,” and then provide subsequent inputs to introduce new aspects to the scene, such as “he writes in his notebook upon reaching the other sidewalk.”
The CSAIL researchers point out that this model could be utilized for various video editing purposes, such as assisting viewers in understanding a live stream in a different language by generating a video that aligns with an audio translation. It may also aid in producing new content for video games or swiftly creating training simulations to instruct robots in new tasks.
Tianwei Yin SM ’25, PhD ’25, a recently graduated student in electrical engineering and computer science and CSAIL affiliate, credits the model’s effectiveness to its combined strategy.
“CausVid merges a pre-trained diffusion-based model with autoregressive architecture typically seen in text generation systems,” explains Yin, co-lead author of a new paper discussing this tool. “This AI-driven instructor model can envisage subsequent steps to train a frame-by-frame system to prevent rendering mistakes.”
Yin’s co-lead author, Qiang Zhang, is a research scientist at xAI and a former visiting researcher at CSAIL. They collaborated on the project with Adobe Research scientists Richard Zhang, Eli Shechtman, and Xun Huang, alongside two CSAIL principal investigators: MIT professors Bill Freeman and Frédo Durand.
Caus(Vid) and effect
Many autoregressive models can produce a video that initially appears fluid, but the quality often deteriorates later in the sequence. A clip featuring a person running may seem realistic at first; however, their legs might start moving in unnatural ways, indicating frame-to-frame inconsistencies (also referred to as “error accumulation”).
Video generation plagued by errors was prevalent in earlier causal methods, which learned to predict frames one at a time independently. CausVid, on the other hand, employs a powerful diffusion model to impart its overarching video expertise to a simpler system, allowing it to generate smooth visuals much more rapidly.
CausVid showcased its video-making capabilities when researchers assessed its proficiency in creating high-resolution, 10-second videos. It surpassed benchmarks such as “OpenSORA” and “MovieGen,” operating up to 100 times quicker than its competitors while delivering the most stable, high-quality clips.
Subsequently, Yin and his associates evaluated CausVid’s capability to produce stable 30-second videos, where it also outperformed similar models in quality and consistency. These findings suggest that CausVid may ultimately be able to create stable, hours-long videos or even content of indefinite length.
A follow-up study indicated that users favored the videos produced by CausVid’s student model over those generated by its diffusion-based instructor.
“The speed of the autoregressive model truly makes a difference,” remarks Yin. “Its videos are as visually appealing as those from the instructor model, but with less production time, the trade-off is that its visuals are slightly less diverse.”
CausVid also excelled when tested on over 900 prompts utilizing a text-to-video dataset, achieving the top overall score of 84.27. It excelled in metrics such as image quality and realistic human movements, surpassing state-of-the-art video generation models like “Vchitect” and “Gen-3.”
While presenting a significant advancement in AI video generation, CausVid may soon be able to generate visuals even more rapidly — perhaps instantaneously — with a streamlined causal architecture. Yin suggests that if the model is trained on domain-specific datasets, it will likely yield higher-quality clips for robotics and gaming.
Experts believe this hybrid system represents an encouraging improvement over diffusion models, which are currently hindered by processing speeds. “[Diffusion models] are significantly slower than LLMs [large language models] or generative image models,” states Carnegie Mellon University Assistant Professor Jun-Yan Zhu, who did not participate in the study. “This new work changes that, making video generation considerably more efficient. This means enhanced streaming speeds, more interactive applications, and reduced carbon footprints.”
The team’s efforts were partially supported by the Amazon Science Hub, the Gwangju Institute of Science and Technology, Adobe, Google, the U.S. Air Force Research Laboratory, and the U.S. Air Force Artificial Intelligence Accelerator. CausVid is set to be presented at the Conference on Computer Vision and Pattern Recognition in June.
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