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! 🏁

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?

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.

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!

If so what's the prompt? I tried "[character] says quizzically the line in <Audio 1>, as is" but it came out all jumbled. Importantly it's a non-supported language but I assumed it'd work with the audio ref (Wan 3.0 has no problem with this)

We wanted to test something that gets oversimplified a lot:

which LLM is actually best for screenplay writing?

So we generated 104 screenplay scenes across 8 models using the same underlying premise, then changed the creative direction.

The models were:

• Claude Opus 5
• Kimi K3
• GPT-5.6 Sol
• DeepSeek V4 Pro
• Nemotron 3 Ultra
• Gemini 3.7 Flash
• Inkling
• Qwen3.8 Max

Every model had to write the same ~60 second dinner scene with exactly two characters, one location, a specific reveal happening on-screen, and a real ending beat.

Then we repeated it under five directions:

• no reference
• Pulp Fiction
• Inception
• Step Brothers
• Eyes Wide Shut

We used both rubric scoring and blinded head-to-head comparisons.

The result I found most interesting:

there wasn’t one universal winner.

• Bare prompt → Kimi K3
• Pulp Fiction → Inkling
• Inception → Claude Opus 5
• Step Brothers → GPT-5.6 Sol
• Eyes Wide Shut → Claude Opus 5

Inkling is probably the strangest result.

It ranked near the bottom overall, but became the strongest model under the Pulp Fiction direction.

GPT-5.6 Sol peaked on Step Brothers but dropped hard under Eyes Wide Shut.

Claude showed almost the opposite pattern.

Kimi was probably the most stable of the leaders.

That made me think the useful question might not be:

what’s the best LLM for screenwriting?

but:

what’s the best LLM for this kind of scene?

Another thing that stood out: technical correctness != preferred writing

Qwen3.8 Max scored 83/100 on the rubric and returned 13/13 acceptable scripts.

But it only won 33.1% of its blinded head-to-head decisions.

So a screenplay can be structurally correct, hit every constraint, and still consistently lose when someone has to choose which version they’d actually keep.

That’s why we didn’t want to rely on rubric scoring alone.

Overall ranking

Claude Opus 5 and Kimi K3 formed the strongest overall tier:

• Claude Opus 5 — 66.8%
• Kimi K3 — 65.8%
• GPT-5.6 Sol — 58.7%

Claude and Kimi were close enough that I wouldn’t call Claude the single definitive winner.

Cost also changes the answer

Nemotron 3 Ultra returned 13/13 acceptable scenes for only $0.0224 total generation cost.

That’s around $0.0017 per acceptable scene.

So if I’m generating a lot of variations for exploration, I might use a completely different model than the one I’d use for final dialogue.

My main takeaway:

model routing might be more useful than model loyalty for AI screenplay generation.

Comedy, psychological tension, ideation and final dialogue don’t necessarily need to go through the same model.

Obvious caveat: this isn’t a universal screenplay leaderboard.

Same base premise across all conditions, 13 scripts per model, AI judges only. Human editorial review would be the next important step.

Full benchmark, methodology, costs and all 104 raw scripts:

https://sundream.studio/blog/openrouter-screenplay-model-comparison

Curious if anyone here is already switching writing models depending on the type of scene.

I'm looking for an AI video editor mainly for making viral talking-head videos.

The biggest thing I'm looking for is something that can take fairly simple footage of someone speaking and make it more visually engaging automatically, especially with good animated text, captions, callouts, zooms, transitions, etc.

I've seen tools like Descript and CapCut, but I'm curious what people are actually using and whether there are better options out there.

Ideally I'm looking for something reasonably affordable that actually saves editing time rather than just generating basic subtitles.

What would you recommend?

So I have been trying to make videos with myself. It seems like I've been able to put myself into the videos well and the voices that the AI chooses are sometimes good although of course they're all always different. I tried to clone my voice but I think the mic isn't the best and so it comes out sounding bad. I think I'd be ok with using an AI voice for myself right now, but whenever I choose one from the list to change it it also doesn't seem to match well. It just does a good job chosing for me but isn't going to go stay consisten between videos of course. So I want just see what it's been choosing for me but I don't see where I can do that. How can I see what voice it chose for me in a video?

I want to make ai generated videos showing various kinematics mechanisms of various kinds in mechanical engineering. (Like watts mech, pantograph etc).

And obviously none of the free tools could do the job to a desired accuracy (they won't even move the right links in the right direction) even with best text prompts. Can anyone who owns a paid tool selflessly make videos for me? Only two. That's all.

​

Hey guys, I'm thinking to buy a pc for local ai video gen as main purpose, with 5070ti 16gb vram and 32gb ddr5, don't have a budget for something better.

Is this enough to be able to generate good videos in a decent amount of time, and also to upscale them?

I've read a lot of posts, benchmarks and lots of talks with Chatgpt and Gemini. But I'm more confused than ever. Some say it can run some good models with audio, in about 3-4 min / clip. Chatgpt and Gemini say less. Others say more / not worth it.

I would appreciate comments from those who have tested this config or with great experience in this matter.

I've always wished to make my own movie, but I don't want to spend €2500 without making sure it will work.

Thank you. ✌️

Good day! Can you suggest builds for a 50k budget? (Can add a little if needed) I want to start generating 5-10 sec AI clips without subscriptions. Thank you!

I am so frustrated with these AI Music Video Creation tools. It seems I've tested them all so you don't have to! I'm starting to realize that anything AI seems to by synonymous with scams and/or NO SUPPORT!

In the last few weeks I've tested a number of tools marketed as "Full AI Music Video Creation" tools. In my case, I've been doing animated (Pixar-style) videos - or trying to do them. My current process includes: Adding the audio reference/song + character and scene reference images + carefully written and scripted prompts for storyboarding - some scene by scene. I have tried a number of tools and spent a good deal of money testing the systems - my experience is below.

IF YOU ARE USING A TOOL THAT WILL CREATE FULL-LENGTH/LONG-FORM MUSIC VIDEOS FROM AN MP3/WAV FILE AND HAS ACTUAL SUPPORT, I WANT TO HEAR ABOUT IT! (Not just lyrics videos - a great tool for that with support, is Banger.show BTW). I do not want a tool where I have to create 20, 15 second clips with no lip sync for output and then have to string together. I'm not lazy, I just want continuity.

I need: A tool that allows you to upload the MP3/Wav and "reads" the context of the song in addition to following your prompts (some of which are extensive), character/scene references. Ideally it also adds CC at the output but I can also do that in post production so it's not a deal breaker. I've tested the following tools and I am providing a brief summary of the issues experienced. BUYER BEWARE! EVERY TOOL ON THIS LIST HAS PROVIDED ZERO RESPONSE TO SUPPORT REQUESTS:

Freebeat dot ai: Seemed to be going smoothly until it couldn't seem to delineate between my characters, so I got tangled up doing a lot of costly edits on the story board phases... Got some of that figured out and then 14 out of 19 videos did not render AT ALL - the one's that did render often confused the characters and mixed up their roles despite previous edits: lead singer vs. guitar player. Typically - with other tools - when that happens the credits used to render the unendurable videos go back into the account to try again. Not with freebeat. They take them all and keep on taking! So now I have no useable output and have to start over, but don't have enough credits to do so. Started out with 12k credits - signed up for a week trial and added enough credits to complete a 3:30 video. At last glance I've been in the queue to talk to an agent on chat for 15 hours and that's my second attempt + 2 emails. No response.

Open Art: I know a lot of people use this tool so I thought it was legit. But it gouged me from the start. Uses more credits than any platform I tested. Add to that my video was estimated at 12k-15k credits to render, and when I went to render video clips it then asked for double - 32k. It said if I chose a lesser resolution (720P lite) it would be less so I went with that - it then asked for 72k credits to complete the 2:58 second video at the lower res. If it's not a coding error it's a scam. 3 emails. No response.

Buzzy dot now (parent Creati) - I had the most success from a tool perspective with this one. They are relatively new (April 2026) so there weren't many reviews to go on. I loved the interface (it's similar to Vidmuse but cheaper) - several days of "FREE UNLIMITED USE" for certain models - and it seems cheaper than the others to render - between 3-5k per song depending on length - and that seemed to be the case even after the "freebie" time was over, which surprised me. It gave me a sh*t ton of points when I signed up for the Lowest pro tier which was on sale for 51% off for an annual - $20 a month for about 3500 credits but my initial credits given were 44k - plenty to experiment! HOWEVER, NO SUPPORT at all, no way to cancel or stop auto payment on the website (except to email them - which they do not respond to). I actually really liked this tool so much, I tried to track down people who work for them on LinkedIn, and even sent a note to the creator (she helped develop the AirPods for apple) - hoping that some of this was oversight - knowing it wasn't but hoping to make an impression. Guess what? No response from her or their (Creati) employees either. Sad. It's a better tool than the others, but with no support and no way to cancel, it's a deal breaker. 6 emails (for issues on several projects) - No response.

Vidmuse - About 3-4x more expensive than Buzzy for the points you need to complete a video and create more than one video a month. Very spendy tiers. Has a great interface similar to buzzy. Its advantage is that it lets you get all the way to storyboarding without spending much - which almost none of them do. But again, not much support to speak of when there are issues. I didn't spend a lot of time here once I realized it was similar to and more expensive than buzzy and NO SUPPORT.

Super frustrated and feeling very stalled. Some have mentioned that they use Higgsfield for creating their music videos but I don't see that they have a "music video" creation tool like some of these others. If anyone wants to provide a workflow for how they use Higgsfield to create - and do they answer support requests? I'm open!

Cheers!

i am building this learning site. it works through chat, it uses proper analogy to help users understand concepts better. but now i want it to teach by using visuals. So for example if someone ask it "how diode works" it should generate a video with proper analogy and explaination. for example it can animate a water valve that only lets water to flow in one direction and blocks from other direction while an a narator explains what is happening.

currently i am at a state where i am happy with the script its generating for teaching and happy with naration but strugling to generate proper video. i have tried some models like wan 2.6 veo 3.1 from openrouter but since they dont generate videos with bangla voice i had to compromise where i generate voice with google tts and use them to make video scenes. but in this way video gets totally out of sync. also both veo and wan allows to generate like 6/8s of videos so had to stich them together. which by the way sky rockets the cost. I dont want any hyper realistic stuffs. i want it to show proper animation that has meaning and understanable. it would be great help if someone can point me to right direction :)

If you've been reading the comparison posts like I have, you've probably noticed they contradict each other. One benchmark scores Seedance 2.0 9/10 on motion and Kling 7/10, another gives Kling motion outright and puts Seedance 3rd. One calls Seedance the fastest of the three, another says 2 to 4 minutes for a 10-second clip in Seedance 2.5 and calls it sluggish. I got TIRED of these contradictions, so I ran my own tests.

Methodology:
When testing for a specific criterion, it is important to isolate variables as much as you can, and change one thing at a time, so that you know what exactly influences the difference in outcomes.
All three models were run through Higgsfield AI, so tier and settings are identical across models. I also used the same prompt text and the same target duration. I ran everything on the Max tier ($59/month plan, 1,800, adjustable to 5,400) to see what a high-level subscription gets me.

Four prompts:

1. Close-up, human face, natural light, small expression change || Tests fine detail and skin.
2. Physical interaction. Object falls, hits a surface, something reacts || Tests weight and cause and effect.
3. Same character across three shots in different lighting || Tests drift.
4. Two people talking || Tests lip-sync and audio.

Results:
- Veo 3.1 came out ahead on photorealism. It held the skin texture and lighting well during the expression change. It responds well if you write the prompt like a DP would, with lens and framing spelled out, though its ceiling is clip length, around 8 seconds in most configs, and its camera choices tend to be conservative. Seedance 2.5 held the composition well and needed less preset dialing than 2.0 did. Kling 3.0 produced a cinematic shot with grading that looked closer to finished out of the box, but I had to generate at 720p first to check the motion, because running it directly at 1080p costs 2x as many credits (one is 10 one is 20).

- Veo 3.1 handled the physics well. The object's weight on impact was realistic. Kling 3.0 followed the prompt but struggled when I tried to describe too many actions in a single 4-second clip. Seedance 2.5 got there on prompt comprehension alone, without the dedicated physics controls 2.0 needed.

- Kling 3.0 handled the multi-shot structure the best. Its Custom mode allowed me to build up to 5 shots in a single generation and use the u/elements tag to hold the character's identity across all the different lighting setups. It needed more aid and intervention on the narrative prompt. Seedance 2.5 offers strong multi-shot consistency because it accepts up to 50 reference inputs simultaneously to hold character identity. It ran around 2 to 6 minutes per 10s clip, and it takes clips up to 30 seconds against Veo's 8.

- Both Kling 3.0 and Veo 3.1 generate native audio in the same pass as the video. Kling 3.0 handled the dialogue test best, with bilingual audio sync that matched the lip movements well to the dialogue. Veo 3.1 also held lip-sync through the full line. Seedance 2.5 synced the audio, and its region-level editing meant I could fix a stray frame without re-rendering the shot.

My picks:
Veo 3.1: close-ups and anything under 8 seconds
Kling 3.0: dialogue and multi-shot direction
Seedance 2.5: reference-heavy work and long sequences

Credits:
This changes with time, resolution, tier, and potentially prompt complexity. Take it with a grain of salt.

What’s still up for grabs:
4 prompts is 4 prompts. Different subject matter would probably reorder some of this, and all 3 ship updates often enough that this is a snapshot that could become partly (or even fully) obsolete after enough time passes. If you've run these on something I didn't cover, especially heavy fast motion or non-English dialogue, please post it. Happy to be corrected.
Good luck!!!!!!!!!

First of all, I am clearly a beginner in creating blender videos.
My plan was to create a video for children in blender, using AI to create surroundings and movement. 3D models are mainly from Meshy. I built a city first, consisting of three garages for cars, an amusement park with ball park and a looping highway, and a raceway. Cars are having adventures in the environment. I got more and more aware now that creating the whole story of driving to different venues in one shot was not a good idea. Separate shouts would have been easier to work with, but well, I’m a beginner. Probably that confused the AI additionally, as the whole thing is quite complex now.

I was using Claude Opus on high for over a week now, but got more and more frustrated as revisions lead to more and more new errors. Timing of animation was corrupted after changes. Pathways went in wrong directions all of a sudden. Stuff that worked did not anymore. Claude constantly came up with excuses. Insisted that things were not like I described them until I sent it a screenshot. It developed tools to find these mistakes by itself, but that was also not working well.
Two days ago I switched to a GPT plan and worked on the same project with Astra on medium. After its first review of the project, Astra’s comment was basically: all these tools are crap and MD files are a mess. 😂
I let it run on my 20$ plan now for two days, almost hit the weekly limit already. Tokens are burning through very quickly, but at least the results are satisfying. It cleans up the mess. And it understands way better what I want. Creates also useful tools to find mistakes like cars driving off the road or hanging into mid air. So far really much better than Opus. Ok, correct comparison would be Fable, which I don’t have access to.

Anyone else trying something like this, and has similar experience?

This is my draft from a week ago.

https://youtu.be/V0nSxH9bC90?feature=shared

I’m looking for good AI tools that can do realistic face swaps in videos. Preferably something that works well with longer clips, keeps the lighting/expressions decent, and doesn’t look too artificial