MLLM Semantic Planner
The planner-style workflow reasons over text, images, source videos, and target placeholders before rendering the final video.
Bernini brings semantic planning into video creation. Turn prompts, source videos, and reference images into coherent AI videos with V2V editing, reference-to-video generation, content insertion, and DiT-style rendering.Turn prompts, source videos, and reference images into coherent AI videos with V2V editing, reference-to-video generation, content insertion, and DiT-style rendering.
Create and edit videos with Bernini. Combine prompts, reference images, source videos, and planned motion for controlled AI video output.
You can describe both the visual scene and the audio/dialogue in this single prompt.
Your generated video will appear here
Build prompt-driven edits, reference-guided variations, inserted content, product demos, and cinematic short concepts with one Bernini workflow.
Use a video generation workflow shaped by semantic planning, multimodal references, and renderer-guided motion consistency.
Powered by Bernini AI Video Generator. Plan video semantics from prompts, reference images, source videos, and placeholders before rendering visual tokens into final motion.
Bernini supports video editing, reference-guided video editing, content insertion, and reference-to-video generation for controlled creative production.
A practical overview based on the Bernini paper: semantic planning, reference-guided editing, content insertion, and reference-to-video generation in one AI video workflow.
The planner-style workflow reasons over text, images, source videos, and target placeholders before rendering the final video.
Generate video in latent space with renderer-style denoising, helping prompts and references become smooth, coherent video outputs.
Guide objects, materials, weather, visual style, and inserted content with references so the output follows the user's creative intent.
See what creators are building with Bernini video workflows. Join the conversation on X to get featured.
Use the Bernini workflow to plan a scene, provide references, and generate or edit video with clear semantic control.
Start with video-to-video editing, reference-guided video editing, content insertion, or reference-to-video generation depending on the result you need.
Describe the target scene and upload source images or videos. Bernini uses those inputs as semantic guidance for the output.
Generate the video, review motion consistency and reference accuracy, then download the final result for creative, marketing, or research use.
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Choose the credit package that fits your Bernini workflow, from quick prompt tests to reference-guided video editing and R2V experiments.
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Answers about Bernini-style video generation, reference-guided editing, content insertion, credits, and output handling.
It is a video creation and editing workflow inspired by Bernini's semantic planning approach. It helps users combine prompts, reference images, source videos, and target placeholders to generate coherent AI video outputs.
This page focuses on V2V video editing, RV2V reference-guided video editing, content insertion, and R2V reference-to-video generation, matching the main workflows described in the Bernini article.
Semantic planning separates high-level scene reasoning from rendering. The planner interprets prompts and references first, then the renderer turns planned visual meaning into video frames with stronger consistency.
Yes. You can use generated outputs for marketing, social media, and business content, subject to applicable laws and platform policies.
After generation, results appear in your workspace and the My Works page. You can re-download them anytime from your saved history.
Temporary provider URLs may expire. We store successful outputs on our own storage to keep stable playback and downloads.
Move from prompt to planned scene to generated video with a workflow designed for editing, references, and coherent visual control.