Reading a Cut: Critique & Finishing
Finish & Screen
Post-production has historically been where AI made its first industrial inroads — colour grading, noise reduction, background removal. The introduction of AI systems that analyse and suggest edits to dramatic sequences is a qualitatively different step, one that the editing community is receiving with suspicion and the studios with interest.
Adobe Sensei and DaVinci's Neural Engine Transform Post-Production — Editors Report 30% Timeline Gains
AI-assisted editing tools like Adobe Sensei and DaVinci Resolve's Neural Engine are now standard in professional post-production. Rather than fully automating edit detection, they reduce technical friction: color correction that once took hours now takes minutes, scene analysis flags continuity problems before assembly, and frame-by-frame logging is automated. Editors report 25–35% faster timelines across 2024–2025 productions, with the tools handling tedious technical work while creative decisions remain human. Limitations remain in complex transitions and stylized editing, where the tools suggest rather than decide.
"The tool detects the cut point; the editor decides if it's the right one."
Source ↗Hollywood Editors Quietly Adopt AI Tools — Fear of Backlash Keeps Adoption Hidden
Editors and VFX professionals across major Hollywood productions are using AI-powered editing and analysis tools, but widespread public secrecy surrounds the practice. As one VFX veteran put it: 'There are tons of people who are using AI, but they can't admit it publicly.' The fear centers on artist backlash and union concerns, even as AI tools like DaVinci Resolve's Neural Engine and Adobe Sensei become embedded in daily workflows. Picture editor Harry Miller notes that AI handles technical suggestions — noise removal, scene detection, color matching — while human judgment remains irreplaceable: 'A computer is not going to know what actor's performance my producer or director likes or hates.' The unspoken adoption reflects an industry equilibrium: tools improve efficiency without (yet) visible credit or public acknowledgment.
"The tools are everywhere; the credit for using them is nowhere."
Source ↗Walter Murch's Six Rules of Editing Remain the Gold Standard — But Can AI Learn Emotion?
Walter Murch's hierarchy of six editing criteria — Emotion (51% priority), Story, Rhythm, Eye-trace, Two-dimensional screen composition, and Three-dimensional spatial continuity — has guided editors for decades. Murch's core principle: 'If you have to give up something, don't ever give up emotion before story. Don't give up story before rhythm, don't give up rhythm before eye-trace, don't give up eye-trace before planarity, and don't give up planarity before spatial continuity.' These rules are now being referenced in AI editing research as a framework for formalizing editing criteria. However, the gap remains vast: AI can detect rhythm violations and continuity breaks; Murch's emphasis on emotion — the non-quantifiable resonance of a moment — remains stubbornly human.
"It has Murch's rules. It doesn't have his reasons."
Source ↗ACES Launches Landmark AI Series — "Legal and Ethical Implications of AI in Editing" Kicks Off May 2025
ACES (The Society for Editing) announced its new Spotlight Series on artificial intelligence in editing, with the first session — "Legal and Ethical Implications of AI in Editing" — held May 20, 2025, on Zoom. The series reflects widespread industry concern about AI's role in post-production workflows and job security. A second session on the practical aspects of using AI in editing is tentatively scheduled for August 2025, and a third session focused on job protection for editors in the AI era is planned for January 2026. Unlike the WGA's 2023 labor agreement — which established that AI cannot be a writer — editors lack equivalent contractual protections and face an open question: how do unions codify roles that are increasingly hybrid (human judgment + machine analysis)?
"Editors got a seminar series. Writers got contract language."
Source ↗If an AI editing system could demonstrably improve audience retention rates on a cut, should a director be obligated to consider its suggestions?
Reading Finishing Cuts
Reading: Finishing a Cut — Recognition and Polish
What does "finishing" mean?
A rough cut is a skeleton. You've assembled shots, threaded sound, locked down pacing. But finishing is the layer that asks: Does this cut speak? Will an audience lean in, or look away?
Finishing operates on two planes:
Reception — what the cut does to a viewer. A cut that lands produces rhythm, builds tension, or clarifies an idea through timing and visual clarity. Rapid intercutting synchronized with musical or vocal cadence creates momentum through repetition and sync. Silence after sound creates weight. These are grammar, not decoration. You read a cut by watching it cold and asking: where does the eye go? Where does the ear follow? Does the pacing feel intentional, or slack?
Polish — removing friction. Rough cuts have visible seams: colour shifts between shots, audio hum, text that catches the eye by accident, or resolution mismatch that breaks immersion. Polish is the work of normalizing these surfaces so the intention shows, not the stitching.
AI upscaling in finishing
Upscaling enlarges video resolution. A 720p source blown up to 4K requires reconstruction — the AI predicts what detail should exist at the finer scale, and fills it in. This is useful finishing work in three cases:
1. Archive footage or UGC (user-generated content). Archival or phone video often arrives at low resolution. If your narrative depends on that footage but it looks unfinished against your native-resolution material, upscaling narrows the gap visually.
2. Unifying mixed sources. A trailer with stock footage, archive, and fresh shoots may carry visible resolution steps. Upscaling the lower-resolution segments to match brings visual consistency — not perfect, but unified.
3. Final delivery at a higher target spec. If your finished cut is 1080p but the distribution channel demands 4K, upscaling is cheaper than reshooting, though the gain is perceptually small at playback sizes most people view at.
Upscaling does not add information that was never captured. It predicts plausibly. On fast cuts or highly stylized imagery, the prediction can miss and produce artifacts — halos, ghosting, or softness. It's a trade: visual consistency at the cost of detail fidelity.
Reading your own cut
After upscaling, watch it cold. Ask:
- Does the pacing breathe? Or does it feel relentless / draggy?
- Where do jumps happen? If they're not intentional, they read as mistakes.
- Is text legible? Title cards, captions, graphics should have breathing room (visual weight around them so they don't drown in busy shots).
- Do cuts land on rhythm or against it? Intentional off-beat cuts create tension; unintentional ones look amateurish.
- Colour and tone consistent? A shot that looks too cool or warm stands out as foreign; audiences sense it even if they can't name it.
This is critical thinking about your own work: not "is this good?" but "does this do what I intended?" Separate those. A cut can be technically accomplished and fail to land its idea. A rough cut can be lo-fi and hit hard if pacing and intention align.
FINISHING THE FILM
What Makes a Cut Screenable
A rough cut is a skeleton. You've locked the shots and pacing. Finishing asks: does this cut speak? Learn to read your own work against five concrete criteria, then fix what breaks the intention.
**Marking guide:**
1. **Pacing.** Look for specific timecodes or descriptions (not generalities like "it was fast"). A solid answer names the *mechanism*—e.g., "the cut at 0:15 lands on the drum hit, so it feels earned; the cut at 0:40 happens between beats and feels jarring." Accept either verdict if reasoned.
2. **Visual friction.** Colour shift, resolution drop, or unexpected text interruption are legitimate observations. If they spotted nothing, ask them to re-watch and pause on any moment where they sensed something looked *off*—they may not have named it at first. No friction = valid; pretending to see something that isn't there = not valid.
3. **Sound/image sync.** Credit any specific moment where they heard the relationship—dialogue hitting a gesture, music swelling on a reveal, silence following action. Sync doesn't have to be perfect; intentional near-miss is still intentional. Vague answers ("the music was good") = redirect to a specific cut.
4. **Legibility.** If text was legible and didn't compete, they passed. If text was hard to read or vanished into the background, they spotted a real polish gap. No text in the video = N/A.
**Critical step:** Did their reading match the creator's intention? If yes, the cut is working. If no, the creator has clarification work to do—either strengthen the visual/sonic grammar or accept that the idea didn't land. This is the insight: *good intent + weak execution = weak cut*, not a character flaw.Task Upscale And Finish
Take a 60–90 second rough cut (yours or a peer's). Identify one moment where resolution is visibly lower than the rest of the sequence, or where colour/tone shifts noticeably. Upscale that segment using an AI video upscaling tool (TopazGigaPixel, Real-ESRGAN, or equivalent). Watch the upscaled version integrated back into the cut, and write a 150–250 word assessment: Did upscaling fix the visual friction? What trade-offs did you observe (e.g., did detail improve but artifacts appear on fast motion)? What would you change for the next iteration—re-upscale at different settings, or return to the original and solve the problem differently (e.g., re-grading, re-framing)?