Generating a video from a text prompt is difficult in part because an AI system must keep objects and scenes consistent as they change from one frame to the next. StableVideo explores a different task: starting with footage that already exists, then using diffusion models to alter its contents while preserving visual continuity.
Editing footage instead of starting from scratch
StableVideo brings some video-editing capabilities to models such as Stable Diffusion. Users can make text-based changes, including altering an object’s attributes, applying an artistic style, or changing a background. The system processes video frame by frame rather than creating an entire sequence from a prompt alone.
That distinction matters because the existing video provides visual structure for the edit. The model can use that structure to guide changes and preserve shapes, while the original sequence offers information about how the scene develops over time. StableVideo is designed to build on both, rather than treating every frame as an unrelated image.
How keyframes help keep edits consistent
The method centers on selecting keyframes and editing them with a standard diffusion model based on text prompts. StableVideo then transfers information between edited keyframes, using the parts of the video that overlap. This process, which the researchers call “inter-frame propagation,” passes object appearances from one keyframe to the next.
In practical terms, a change made to an object in one keyframe can inform how that object should appear in later parts of the sequence. The model uses visual structure to help retain shapes, while information carried across keyframes helps guide the generation of subsequent frames. The goal is to avoid an edit that looks convincing in one still image but changes unpredictably as the video continues.
Afterward, an aggregation step combines the edited keyframes into foreground and background video layers. Those layers are composited to form the final result. Separating the elements in this way gives the system a route from individual edited frames to a modified sequence.
What the experiments show
The team demonstrates text-based edits that change object attributes and apply artistic styles while maintaining visual continuity through the video. These examples point to a use for diffusion models beyond generating new images or clips: they can also serve as tools for modifying visual material that has already been captured.
Consistency is central to the approach. A video edit needs to make sense not only in a single frame but across the sequence. By passing information between keyframes and combining edited layers, StableVideo aims to make changes persist as the footage moves forward.
The method also reflects an ongoing challenge for generative video systems. Producing realistic, temporally coherent video from text prompts remains difficult, and even state-of-the-art systems such as those from RunwayML still show significant inconsistencies. Working from existing footage offers a different starting point, but it does not remove every difficulty.
Limits remain for challenging motion
StableVideo’s results still depend on the capabilities of the underlying diffusion model, according to the researchers. The quality of an edit is therefore tied to what that model can handle, even when information is transferred between frames.
The researchers also report that consistency can fail for complex deforming objects. When an object changes shape substantially, its appearance may be harder to carry reliably across the sequence. The authors say this limitation requires further research, so StableVideo should be understood as an approach with demonstrated editing capabilities rather than a solution to every video-editing challenge.
More information and code are available on the StableVideo GitHub page. The project offers a way to explore how diffusion models can modify existing videos, with frame-to-frame continuity as a core part of the process.