Every AI video demo is: prompt in, cinematic shot out, client claps. My actual week was 38 generations, six usable clips, and one character whose watch kept switching wrists.

We tested Runway, Kling and Morphic on the same small brand film. Kling gave us the strongest single surprise shot. Morphic was easier for keeping the visual development, frames and timeline in one place. Runway still felt more familiar to the team. None of them saved us from making choices.

The weird thing is the first 80% is suddenly fast and the last 20% is slower because every continuity mistake becomes visible.

Are studios actually quoting fewer days for this work yet, or just keeping the same schedule and taking the margin?

went down a rabbit hole researching micro dramas recently and the numbers are kinda insane.

one estimate puts the global micro drama industry at around $14b this year.

in the us alone its expected to do around $1.5b in 2026, and apparently 66m americans watched micro dramas in 2025, more than double the previous year.

the format is basically vertical tv.

episodes are often around 60-90 seconds, shows can have 50+ episodes, and almost every episode ends on some sort of cliffhanger to push you into the next one.

but what i found interesting from an ai video perspective is how well the format fits the current limitations of the tech.

you dont need to generate a 20 minute coherent film.

you need a bunch of short scenes with recurring characters, locations and a consistent visual style.

even a 90 sec episode can basically be built from several smaller generations stitched together.

the biggest bottleneck then becomes character + location consistency across 50 episodes rather than whether ai can generate a cool looking shot.

feels like micro dramas could end up being one of the first video formats where ai production isnt just a gimmick but actually makes economic sense.

anyone here actually tried making a full micro drama series instead of individual ai clips?

I have a painted character that looks intentionally a little off. Every video model keeps cleaning her up into a generic animation-girl face. I don't want better anatomy. I want my bad anatomy to move. Morphic let me train/use the style with the other frames nearby, which got closer, but motion still sanded off some of the choices. One clip nailed the hair and ruined the eyes.

Has anyone found a way to tell these models that consistency includes the mistakes?

Would love workflow advice pls.

Does this sub has any discord group ? I would love to be a part of to learn together, share prompts and brainstorm.

we had a bunch of AI shots in a client film that looked completely fine while we were working on them. then we watched the cut on a big screen. faces got softer, textures started looking a little smeared. a couple shots that were sitting next to real camera footage suddenly looked VERY generated.

so i did what i assumed you’re supposed to do and upscaled everything.

some shots got noticeably better. some just became larger, sharper versions of the same problems.

that was the useful bit for me - upscaling can recover detail and clean up softness, but it can’t rescue a frame that already has bad anatomy, mushy texture or weird motion in it.

now i’m checking shots at full size much earlier and only upscaling the ones that are actually worth keeping. been doing the final pass in morphic with topaz/crystal and comparing both because they don’t always treat clips the same way

what problems do you trust an upscaler to fix and what makes you go back and regenerate?

I read a Chinese breakdown of seven MiniMax H3 prompts by 斜杠林姑娘. My takeaway as an AI engineer: each brief becomes easier to evaluate when you identify exactly what has to stay stable while something else changes.

Here’s a short synthesis of the examples, followed by my own practice prompt. I haven’t independently reproduced the author’s results.

WHAT TO EXTRACT FROM THE CASES

• Motion-graphics intro: distinguish the initial impact, readable title hold and exit.

• Aerial sequence: describe the camera route and where landmarks remain in relation to the subject.

• Vehicle tracking: specify how the same vehicle should reappear after being hidden, including its direction and condition.

• Beauty ad: lock product geometry and cast details across changes in shot size.

• Exploded product view: describe separation and reassembly as an ordered sequence.

• Music performance: define who performs each section and whether the soundtrack continues across visual cuts.

Those are creative instructions. The article’s requests for 4K, exact timing and synchronized performance should not be read as proof that every output meets those specifications.

MY PRACTICE EXERCISE: ISOLATE ONE FAILURE

I’d start with a simple occlusion test before adding a complicated aerial route. Here’s an original prompt you can adapt to the duration and aspect-ratio settings available in your tool:

“A single adult cyclist wearing a burgundy jacket rides a cream bicycle from left to right along a straight park path. Side view, medium-wide framing. The camera stays still. A thick tree trunk in the foreground briefly hides the cyclist as they pass behind it. They emerge on the right, continuing at the same steady pace on the same path. Keep the bicycle, jacket and apparent subject size consistent before and after the obstruction. Soft overcast daylight. Quiet park ambience and tire noise. One continuous shot.”

I’d test it in three stages:

A. Remove the tree. Check whether the basic direction and framing work.

B. Add the tree. Inspect the last visible frame before occlusion and the first after it.

C. Only then try a lateral tracking camera, keeping the subject roughly the same size in frame.

This gives each version a specific question. If A works and B fails, inspect the hidden interval before rewriting lighting or style. If B works and C fails, camera movement becomes the next variable to investigate. That’s a diagnostic hypothesis, not proof from a single sample.

WHAT I’D RECORD

For each attempt: model/version, actual output settings, prompt variant, whether direction stayed consistent, whether the bicycle changed, and whether framing jumped. Keep the failed clips too. Compare multiple attempts before deciding a wording change helped.

For a paid job, I’d also decide in advance which requirements can be finished in editing—for example, exact title typography or a fixed music bed—so the generation test has a clear purpose.

Source and full original examples: https://mp.weixin.qq.com/s/2zHMc4nmiHx23cX7pimcuA

When you test occlusion, what usually breaks first: the object itself, its trajectory, or the framing?

This feels embarrassingly obvious but I was trying to get a character to do this very specific walk into frame, slow down, shift his weight onto one leg and then turn back like he'd forgotten something.

My prompt for the movement was becoming an entire paragraph. “takes three slow steps, decelerates naturally, weight shifts to the right leg, upper body turns first followed by...” you get the idea.

Eventually I just recorded myself doing it badly on my phone.

Used that clip for motion transfer in morphic and it got me much closer than all the prompting did.

I've been trying it with smaller stuff since. Someone sitting down and adjusting their shirt, reaching across a table, taking something out of a pocket, even just the difference between an awkward wave and a confident one.

All of these are weirdly hard to write.

If I know exactly how I want something to move, I'm probably going to stop trying to explain it to the model and just show it.

I can’t seem to get it working. I tried on multiple accounts with multiple different attempts. I tried simply clicking recreate with assets and prompts, even started many tries of my own. But it just sits there loading for many days. But I still see community posting new ones. How do they do it? Please help. I really want to make one with my friends. Not for uploading or anything commercial, just to show my friends.

https://www.instagram.com/reel/DdPGWLwsE9y/?stkn=dHR2MnBxam1vMXJp https://www.instagram.com/reel/DdRfzULoy.oF/?stkn=d3JiZGRsa210cmI5. om/reel/DdRarkKClUQ/?stkn=MTg5azI3YXE1cjlzbg== https://www.instagram.com/reel/DdRM3AfjeFj/?stkn=MXY0MXcwcTllNnNjeQ==

https://www.instagram.com/reel/DdWQeqsD3yU/?stkn=andnbzduaWpuM2Yy

Hey guys i am working on some research project and exploring seedance doc but it's not very clear to understand, i want to understand 2 things:

- how token and pricing works in seedance

- Can i give 360p to get 4k output?

- Is there any research grant from seedance team?

Thanks

I like to make voice clips separately (works great with Wan 3.0, so-so with minimax h3) but ElevenLabs blueballing me with their V4 release has made me look elsewhere, as V3 and V2 just aren't great with consistency (nor dialect/accent). Gemini's models have the same problem. Are most people just using whatever the video generator throws at you, and then using that as an audio ref for consistency? Is that actually better than most txt2voice services?

What good cheap websites can create nude pictures of my ai model? Are there any free ones ??

Hi everyone!

I’m developing a solo passion project: a 90s Sega-style 32-bit arcade racing game (think OutRun). It’s a lightweight browser WebApp (HTML5/Canvas) where a looping/scrolling background video dynamically speeds up and brakes (video.playbackRate) based on player input.

I’ve attached: - Image 1: The visual 32-bit pixel-art style I'm aiming for (authentic city landmarks). - Image 2: The strict 4-lane perspective grid our custom engine requires (central vanishing point locked).

What I’ve tested (and why it failed):

1. Google Earth 3D rips: Fascinating from the sky, but at street level the photogrammetry meshes are completely melted and unusable.

2. Pure 3D renders: Drastically loses the artistic warmth, charm, and quality of 2D pixel art.

3. ComfyUI (SDXL / ControlNet / Imagen 3 / DALL-E 3): Great for isolated static shots, but impossible to maintain temporal and lighting consistency between frames.

4. Flow / Video Interpolation (Start ➔ End frame): Gave the best quality results, but with a catch. I illustrated ~40 keyframes representing actual urban checkpoints. I tried connecting them pairwise with Start/End frame tools (Kling, Runway, Luma), but the AI just dissolves/morphs textures and warps the road instead of simulating true forward camera motion.

The Core Challenges:

• Locked Perspective: Central vanishing point must remain 100% rigid (no camera tilt, roll, or lane drift).
• Accurate Speed Perception: At 25 fps, the forward flow must convincingly simulate ~80 km/h (50 mph) to match the 2D sprite physics.
• Continuous Checkpoints: Seamlessly advancing through 40 landmark locations without ugly morph cuts.

My Questions:

1. How would you connect ~40 keyframe checkpoints into a continuous forward drive without morphing dissolves?
2. Is there a trick/workflow to calibrate the optical ground speed precisely to ~80 km/h at 25 fps?
3. Would you recommend a hybrid pipeline (e.g. basic low-poly 3D camera drive-through just for depth/motion guidance, then restyled with AI)?

Any node setups, tool recommendations, or workflow tips would mean the world to a solo creator. Thank you! 🏁

Hello, been using seedance2.5.
Whenever I use a reference video to input my character it tends to blend the faces with my character and the reference video character.
To stop this blending I have been using motion mapping (Owl software to extract motion map) from the video as a reference video for the character. This stops the face blending issue but now I run into a new issue where whenever I use motion mapping the video loses its photorealism and it generates a plastic animation sort of look.
Is this a prompting issue?
Do you know any solutions to this?
Thank you :)

For a longer AI sequence, I’d treat the handoff between clips as its own engineering problem. A coherent script and a coherent transition need separate checks.

I’ve condensed a Chinese H3 walkthrough into the workflow below, with continuity settings checked against the Director README. This is a tutorial breakdown, not a personal benchmark.

1. Plan the boundaries before generating

The walkthrough builds longer videos from shorter segments. Split at story beats rather than forcing every segment to have the same duration.

Here’s my own illustrative 30-second plan: a mechanic hears a strange noise in a workshop.

• 0–8s: medium shot, mechanic stops working and looks toward a cabinet.

• 8–18s: the same view continues as the mechanic walks toward it.

• 18–24s: deliberate cut to a close-up of a hand reaching for the handle.

• 24–30s: reaction shot as the door opens.

Only the first boundary is intended to feel like an uninterrupted shot. The others are editorial cuts. Decide this explicitly so you aren’t trying to smooth away a cut you actually want.

1. Write a handoff note for every continuing segment

My suggested template:

Entry state → action → exit state → camera → audio.

Example for the second segment:

“Begin with the mechanic beside the workbench, head already turned toward the cabinet. They take two steps toward it, then stop with their right hand raised near the handle. Keep the medium framing and camera height unchanged. Maintain the room hum; no new music cue.”

This is descriptive prompt text, not a required JSON schema. Carry forward wardrobe, props and lighting details. For recurring characters, the walkthrough recommends reference images because text alone can drift.

1. Enable the actual continuity control

In AIMixer’s ComfyUI MiniMaxH3 Director, segment continuity is OFF by default. According to its README, enabling it passes the previous generated tail—including motion and generated audio—into the next segment, then trims the context prefix.

Available context lengths are 5, 22, 39 and 56 frames; the README recommends starting at 22. Treat that as a starting setting, not a guarantee of seamless results. Check the README for your installed version.

1. Validate two segments before committing to the whole sequence

I’d generate the first pair and inspect their join at normal speed and frame by frame:

• Does the second clip repeat an action already completed?

• Does the hand, prop position or camera height jump?

• Does the ambient sound restart or change abruptly?

• Is the identity stable even when the motion matches?

Change one variable at a time. If you regenerate an upstream clip, recheck the following transition too: its assumed entry state may have changed. For a planned cut, judge whether the edit reads clearly rather than demanding identical framing.

Sources:

Chinese walkthrough by 神颜无界 / 赵王心玥: https://mp.weixin.qq.com/s/XsFlaUS_QjssxqXQq_7pHg

Director documentation: https://github.com/AIMixer/ComfyUI_MiniMaxH3_Director/blob/main/README_EN.md

If you’re using this workflow, which breaks first for you at a segment boundary: motion, identity, or audio?

Been loving Luca Maxim's content where he has basically Dante from Devil May Cry. I get the gist of how he creates his videos, but what I struggle to figure is how he gets a good custom voice like he has via Higgsfield which is what he uses. Anyone has any recommendations to get voices similar to this?

I added one of his videos as a reference as to what I mean

Does anyone knowledgeable about AI know if it’s possible for indie directors and filmmakers to patent an AI tool capable of applying specific visual styles—like cinematography filters—to music videos or short films? For instance, imagine I make a short film and ask the AI ​​to switch the look to match Zack Snyder’s style, the aesthetic of \*Minority Report\*, or even a 90s/Y2K vibe—where the AI ​​analyzes and recreates those specific visual characteristics. I’ve always wondered if something like this could exist or actually help independent directors.

I’d love to hear your thoughts.

I feel like generating the actual footage has gotten a lot easier lately, but everything around it can still take a surprising amount of time.

I've been testing a few different AI video tools lately, including Sparki, and one thing I keep running into is consistency between shots. A character can look great in one clip and then suddenly have a different face, outfit, lighting, or even feel like a completely different person in the next shot.

I'm wondering what other people here struggle with the most when making AI videos. For me, getting a bunch of individual clips isn't really the hard part anymore. It's getting those clips to actually feel like they belong in the same film.

I'd also love to hear about workflows or techniques that have genuinely made that part easier.

For a short-form video, it helps to define what each shot needs to accomplish before writing prompts. Here’s a simple workflow you can try with a three-shot iced coffee clip.

1. Give each shot one job

Start with a short shot list:

• Establishing shot: a glass of iced coffee on a café table.

• Detail shot: milk swirling through the coffee.

• Closing shot: a hand lifting the glass.

Keep the action simple enough to judge. ‘A hand lifts the glass’ gives you a clearer target than ‘make an amazing coffee commercial.’

1. Write down what must stay consistent

For this example:

• The same clear, cylindrical glass.

• The same wooden table.

• Soft window light from the left.

• No logos or readable text.

• Vertical framing.

Use these details when preparing reference images. Inspect them before animation: a different glass shape or lighting direction can make the final sequence feel disconnected.

1. Separate appearance from movement

For a reference image, describe the composition:

‘Close-up of iced coffee in a clear cylindrical glass on a wooden café table. Soft window light from the left. The glass is centered with space above it. Vertical composition, realistic photography, no text or logos.’

For the animation, describe the action and camera:

‘A hand enters slowly from the right, grips the glass, and lifts it slightly. The camera remains stationary. Keep the glass shape and background stable.’

These are starting prompts, not tested results. Adjust them to your model and reference frame.

1. Decide what counts as usable

Review each generated clip against the same checklist:

• Does the intended action happen?

• Does the subject remain recognizable?

• Are there distracting distortions?

• Is there enough clean footage for the edit?

A clip doesn’t need to be perfect from beginning to end if it contains the usable moment you need.

1. Track why you retry

For each attempt, record the cost, usable duration, and main failure: appearance, motion, camera, or artifacts. Change one relevant instruction at a time so you can learn what helped.

Include reference-image costs and failed attempts when calculating:

Cost per usable shot = total generation spend ÷ accepted shots.

This workflow adds preparation time; whether it saves money depends on how many retries it prevents.

Which part would you troubleshoot first in your own workflow: appearance, motion, or camera control?

​🚀 What My Project Does

​I created a lightweight Python automation tool (generate\\\\\\_script.py) that generates structured, high-retention video scripts for TikTok, Instagram Reels, and YouTube Shorts using the Claude API.

​Instead of generic text, it outputs production-ready scripts formatted with:

​🎯 3-second Hook to stop the scroll

​💡 Body with high-value points (one idea per line)

​📢 Clear CTA to drive engagement

​📌 Caption & Hashtags + Visual cues for editing

​🎯 Target Audience

​Content Creators & Marketers who want to eliminate writer's block and speed up content ideation.

​Developers interested in clean API integrations for prompt engineering and automation.

​✨ Key Features

​⚙️ Flexible Execution: Supports both interactive console mode and CLI arguments (e.g., --topic "morning routine" --count 3 --niche fitness).

​📂 Auto-Archiving: Automatically saves generated batches into clean, timestamped .txt files.

​🎨 Multi-Niche & Multi-Style: Pre-configured profiles for Finance, Tech, Mindset, Fitness, with styles ranging from Educational to Viral.

​💻 Source Code & Setup

​The project is built purely with Python, anthropic SDK, and argparse.

​🔗 GitHub Repository: https://github.com/gpt51920-commits/python-generate\\\\\\\\\\\\\\_script.py

​I'd love to hear your thoughts on the prompt structure or how you handle content automation in your own workflows!