AI Video Editing

Editing existing video footage with AI used to mean exporting frames, running them through an image editor, and stitching the modified frames back into a clip with manual flicker reduction. The newer generation of AI video edit models — Runway Aleph, Kling O3 video-edit modes, Wan — operates directly on motion footage. You can restyle a scene, replace an object across frames, extend the clip past its original endpoint, or change the entire visual treatment without going frame-by-frame. The capability is real; the workflow is fragmented.

Most AI video edit tools live in their own tabs and own export pipelines. Restyling a clip in one tool, then needing an extension on a different one, then needing object replacement on a third, means downloading and re-uploading at every step — and every re-encode degrades the footage further. The shipped result rarely matches the original quality, and the edit lives in a folder of intermediate MP4s rather than inside a real timeline.

Then there is the integration problem with traditional editing. AI video edits land as standalone clips, but real video work sits inside an edit — alongside your other footage, your audio mix, your motion graphics, your transitions. Without a workflow that places AI edits into a sequence and exports as a real timeline, the AI work stays segregated from the rest of the cut and editors end up rebuilding the timeline manually.

Martini chains video editing nodes on the canvas like any other generation step. Drop the source clip into a video node, wire it into a Runway Aleph restyle node, then chain into a Kling O3 video-edit node for object replacement, then into a Wan extension node to push the duration. Each step reads the previous one as input, the canvas keeps the lineage visible, and the chain runs as one workflow instead of four separate uploads. No quality degradation from intermediate transcodes, no folder of orphan files.

Timeline and sequence integration is the unlock for real editing. The canvas treats every edited clip as a sequence-aware asset — drop edited shots straight into a sequence builder where they sit alongside untouched footage, audio stems, motion graphics, and CTA frames in cut order. Edits are not orphan generations waiting to be reimported; they live inside the timeline from the moment they finish rendering. Trim handles, in/out points, and cut-aware transitions live on the same surface as the edit nodes, so the editor never leaves the workspace to assemble the cut around the AI work.

NLE handoff and edit-template reuse close the loop on real production work. NLE export ships the whole timeline into Premiere Pro, DaVinci Resolve, or Final Cut Pro at clean frame rates and codecs that drop in natively as a real cut, not a folder of mismatched MP4s. The handoff arrives editor-ready: clip names, cut points, and audio sync all preserved. Save the canvas as a reusable edit template and the next campaign loads the same edit chain — same restyle pass, same trim sequence, same NLE export configuration — so a team that ships fifty videos a quarter rebuilds zero of the editing infrastructure between projects.