AI Animation
We use neural networks for video creation — wherever it speeds up the result
Step 1
First, we’ll determine where AI can truly help and where traditional CG is the better choice. Not every task is suited for neural networks, and we’ll be upfront about that from the start.
AI excels at testing, prototyping, generating variations, and tasks where speed matters more than total control. If you need control over every single pixel, we’ll recommend a traditional pipeline or a hybrid approach.
Step 2
The quality of the AI output depends directly on the input we provide. That is why we prepare the foundation —3D models, textures, references, and storyboards — before generation begins.
If the shape or product is unique, we create it manually in 3D and use AI for animation or effects. This allows us to maintain control where it is critical while gaining speed where possible.Step 3
We launch the generation process: AI animation, simulations, and the creation of backgrounds or variations. A key rule applies here: one simple action per shot. The simpler the task for the neural network, the more stable and realistic the result.We go through several iterations, select the best options, and refine the prompts. If the result isn’t satisfactory after three or four iterations, we switch to manual refinement to avoid inflating the budget.
Step 4
AI rarely produces a final result on the first try. We refine the generated material by removing artifacts, adjusting physics, and blending it with the other elements of the video.
For hybrid projects, we integrate the AI-generated components with traditional CG and live-action footage so that the seams are invisible.Step 5
Once all the elements are ready, we assemble the final cut, add sound design, and export the video in the required formats.
The result is a video delivered on time and within budget — without compromising on quality, because we only relied on AI where we trusted it to deliver.