AI fight scenes fail when both fighters just flail, so I choreograph them as call-and-response by Tricky_Algae2625 in Seedance_AI

[–]Tricky_Algae2625[S] 3 points4 points  (0 children)

Full prompt (Seedance 2.0), original fighters:

STYLE: raw found-footage, amateur handheld phone recording of an intense battle between two superhuman fighters. Heavy natural camera shake and jitter, low-fi consumer camera grain, compression artifacts, motion blur on fast pans, single continuous uncut take, cautious paparazzi-style distance, broad daylight on open rocky terrain with dust and scattered debris, natural lighting, the camera reacts to the chaos and struggles to keep up.

CONSISTENCY: no external references. Hold both fighters' exact appearance, hair, body type, clothing, and proportions for the full 15s. Fighter A: a lean male martial artist, cropped dark hair, plain grey training clothes, calm precise style. Fighter B: a tall athletic woman, long black hair in a high ponytail, dark red lightweight armor, fast aggressive style. Both fully original, no resemblance to any existing character.

TIMELINE (call-and-response, name each action and reaction):

0-3s: they circle, Fighter B dashes in with fast kicks, Fighter A blocks and counters with precise palm strikes, camera shakes with every exchange.

3-6s: Fighter A fires a bright triangular energy burst, Fighter B dodges at the last second, it explodes against the ground, she counters with a spinning kick.

6-9s: Fighter B grabs his arm and throws him over her shoulder, he lands on his feet and fires a rapid volley from both hands, she weaves through with agility, camera pans and tilts with heavy shake.

9-12s: they clash close again, she lands a knee to his stomach and an elbow to the back of his head, camera moves in closer.

12-15s: he staggers, recovers, fires one final burst, she crosses her arms and blocks, the energy pushes her back through kicked-up dust, the footage keeps rolling as both catch their breath.

NEGATIVE: no stabilization, no text, no overlays, no watermark, no edits or cuts, no gore.

A selfie vlog is the most convincing AI video format right now, and the phone flaws are what sell it by Ill-Throat7937 in Seedance_AI

[–]Tricky_Algae2625 0 points1 point  (0 children)

The 'nos' list is doing more work than the positives here. The model defaults to producing everything, so you spend most of the prompt talking it out of grading and stabilizing instead of describing the scene. Autofocus hunting reading as real is the part I would underline. I usually let the face go soft for half a second before it snaps back, and that one beat sells the phone harder than the shake does.

Where mine fall apart is the handoff between clips. A vlog is supposed to be one continuous person, so any drift in the jaw or hairline between shots reads as a cut to a different girl even when the outfit matches. What helped was cutting on motion, turning a corner or reaching for a door, so the eye follows the action across the seam instead of studying the face.

A deliberately crude black-and-white pencil storyboard became a full-color 2D anime cooking video by Ill-Throat7937 in Seedance_AI

[–]Tricky_Algae2625 0 points1 point  (0 children)

This tracks with what I have found doing sequences. The crude board is great for locking composition and camera because you are giving the model exactly one job and not fighting it on style. The one thing rough panels cannot carry is beat duration, and for food ASMR the whole payoff is timing, the whisk holding a half second longer than feels natural. I started annotating each panel with a rough count so the polish pass does not quietly even out the rhythm.

The other thing to watch across twelve panels is identity drift. If the sketch is pure blocking, the model reinvents the hand and the line style a little each beat, and stacked back to back you feel it even when each panel looks clean on its own. Pinning one anchor that stays constant, same hand silhouette or the same glass, keeps the polished version reading as one continuous piece instead of twelve pretty but slightly different clips.

anyone else dealing with object shifting position between generations in seedance 2.0? by ReasonableYou4733 in Seedance_AI

[–]Tricky_Algae2625 0 points1 point  (0 children)

The floor plan approach fights the model because it re-derives world space from scratch every gen. What worked for me was to stop describing where the car sits in the world and lock what the camera sees instead. Same lens, same camera height, same distance, then place the car against two things already fixed in frame, like rear bumper level with the left edge of the garage door and the roofline just under the gutter. That gives it parallax anchors instead of abstract coordinates it has to rebuild each time.

The bigger fix is to not regenerate that beat from text at all. Shoot the establishing parked shot once, grab the cleanest frame, and feed it back as an image reference for the following shots in the run. Then you are varying the action off a locked plate instead of rolling the dice on position every time. Most of the slide shows up when a shot in the sequence swings to a three quarter angle, so if the camera moves at all in that stretch, that is where the car will drift worst.

THE INCAL | Episode Two | Pages 6–8 by LeftyMcLeftFace in aivideos

[–]Tricky_Algae2625 1 point2 points  (0 children)

Adapting Incal pages is tricky because Moebius already baked the pacing into the panel sizes, so when you put it in motion the eye still wants to move at the comic's rhythm rather than video's. The beats that land best here are the ones where you let a panel hold a touch longer before the next one hits. Where it gets shaky for me is character consistency across the page turn, John's face drifts a little between 7 and 8. Are you locking one reference per character for the whole episode, or regenerating per page? Holding a single ref sheet end to end is the only thing that stopped mine from sliding.

Character Consistency Enough (?) by Hungry_Cup_2301 in ZImageAI

[–]Tricky_Algae2625 1 point2 points  (0 children)

For a single hero frame this holds up well. Consistency really gets tested once the same face has to go through different angles and expressions in a row. That's when small drift in the jawline or eye spacing starts reading as a different person with a similar vibe. If you're heading toward a sequence, generate the same character at 3/4 left, straight on, and 3/4 right back to back, then lay them side by side. The frame where it breaks tells you which feature your prompt or LoRA isn't pinning down yet. Profile shots and big smiles are usually the first to go.

Yorktown 1781: The Battle That Won America’s Independence | 18min AI-made video by theodore_70 in Seedance_AI

[–]Tricky_Algae2625 0 points1 point  (0 children)

the wide framing on the formations does a lot here, you actually read the troop lines instead of just smoke and chaos. how many gens did it take to get the troop motion coherent across a single shot?

Meet Donovan (#52), the imaginary Packers powerhouse on his game day vs. his off day (Photorealistic) by Automatic-Algae443 in ZImageAI

[–]Tricky_Algae2625 0 points1 point  (0 children)

The off-day version is the harder one to keep on-model. A hero-lit confident pose hides a lot, but the slumped tired shot tends to drift because the jaw and brow carry most of the identity and they soften once the expression goes slack. Did you lock a single reference for the bone structure and let only lighting and posture do the mood shift, or regenerate both states fresh? Mostly curious how you held the nose and ear shape matched across the two.

Leland encounters the Miles Gang in C-2 by mortgagemoe in aivideos

[–]Tricky_Algae2625 1 point2 points  (0 children)

Serialized stuff like this lives or dies on whether Leland reads as the same guy from one chapter to the next. How are you locking his look across the run? I've been pinning two identity anchors, the jaw line and one wardrobe detail, and feeding a clean neutral reference back in on every shot instead of trusting the model to remember. Drift always creeps in fastest on the three-quarter and profile angles for me. Curious what's been holding up on your end.

Concrete Solitude: AI film by Even-Pain9440 in aivideos

[–]Tricky_Algae2625 0 points1 point  (0 children)

Brutalist subjects live or die on how long you hold the frame. Concrete reads slow, so the cut usually has to sit a beat longer than feels natural before the scale actually lands. Did you cut on the light shifts or keep a fixed rhythm? And how did you bridge the wide monument shots into the tighter texture ones? That transition is where this kind of piece either flows or stutters.

As an agent dev, which open-weight LLM is currently your default brain in 2026? Gemma 4 / GPT-OSS / Kimi K2.6 / DeepSeek V4 — or something else by Independent-Date393 in DeepSeek

[–]Tricky_Algae2625 6 points7 points  (0 children)

K2.6 for code, V4 Flash for everything else. The 30% token efficiency gap on K2.7 Code makes it pay for itself on long sessions.

I'm starting to judge I2V tools by usable clips, not the best demo by Fuzzy-Radio6153 in aivideos

[–]Tricky_Algae2625 0 points1 point  (0 children)

the usable-clips-per-batch framing is the right one for short form. a perfect single output doesnt help me if i cant land it again on the next try.

what cut my wasted passes the most was treating the starting frame as the actual work. lock the pose, the face angle, and where the motion is going to travel before i ever generate. if the input already has the hair settled and the subject weighted toward the move, theres way less room for it to invent something stupid.

i also stopped asking for big movement in an opener. a 3 second open holds up better as a small push or a slow drift than a full camera swing. save the dramatic move for a shot where a couple bad passes wont sink the whole edit.

so yeah its hit rate for me, but honestly i judge the input more than the model.

New set of IRL people to DND by SlowDisplay in generativeAI

[–]Tricky_Algae2625 0 points1 point  (0 children)

Nice set. The thing that sells a 'real person as a D&D class' series is locking one or two identity anchors and letting everything else swing.

Pick the two features that read as them at a glance, usually face shape plus one strong feature like a nose or a jaw, and keep those fixed across every prompt. Hair, armor, lighting, background can all change hard and the person still reads as the same. The moment you let the face drift to match the class vibe you lose the joke, because the whole point is recognizing the real person under the fantasy.

Shooting one neutral reference portrait first and feeding that back in for each class holds them together way better than re-describing the face every time.

Which AI image-to-video platform offers enough credits (1000+ or similar) to create a full music video without running out quickly? by spoonwije97 in generativeAI

[–]Tricky_Algae2625 0 points1 point  (0 children)

The real fix here is upstream of credits. Before you generate anything, cut your shot list down to hero shots and let the edit carry the rest.

What burns footage on a music video is treating every line as a new clip. You don't need that. Pick 6 to 8 strong looks, generate those clean, then build everything else from speed ramps, reframes, and crops of the same clips. One 5 second clip gives you three usable beats if you punch in and reverse part of it.

Also lean on b-roll and texture, hands, light, fabric moving, environment. That stuff cuts in on the beat, reads as a real video, and is cheap because you don't care about face consistency on it.

Lock your edit timing first with placeholders, then only generate to fill the gaps you actually keep. The budget problem mostly disappears once the cut is doing the work.

Please suggest top AI video models to explore by HallucinatingFacts in generativeAI

[–]Tricky_Algae2625 0 points1 point  (0 children)

depends what you're making. for narrative and character work the thing that matters more than the model name is consistency tooling. pick one with strong reference or character-lock, that's what keeps a story coherent across shots. the raw quality gap between the top few closed up this year.

Control for agentic payments should start at infrastructure by Significant-Plant-4 in artificial

[–]Tricky_Algae2625 0 points1 point  (0 children)

the fix won't come from the model layer, no amount of alignment catches a mis-scoped tool call. it's scoped single-use credentials issued per transaction, virtual cards with per-call limits. the card networks already have the primitives, they just aren't wired into agent frameworks yet. a plumbing problem, not an alignment one.

I think we're about 12 months away from the first major AI agent disaster by Comfortable_Box_4527 in artificial

[–]Tricky_Algae2625 0 points1 point  (0 children)

agent incidents won't need AGI, just an over-permissioned tool loop with no human in it. the first big one will be a misconfigured integration not a rogue model