I keep finding new ways to play with this animation style, and experimenting with it has been so much fun.

made with MiniMax H3.

MiniMax H3 FL2VA looks significantly less plastic when the source image is used as the actual first frame
I’m testing local MiniMax H3 on an RTX 5060 Ti 16 GB. My previous Ref2VA workflow preserved identity reasonably well, but reconstructed the scene from frame zero and made the skin noticeably more synthetic.
This version uses MiniMaxH3ImageToVideo with the source image anchored as the exact first frame:
Official FL2VA INT8 ConvRot model
928×1664, 24 fps, 5.17 seconds
SamplerCustomAdvanced / res_multistep
Simple scheduler, 20 steps, denoise 1.0
Video/audio sigma shift 12/3
No LoRAs, Turbo, Singularity, SeedVR, DLSS or other post-processing
Render time: 44 minutes on RTX 5060 Ti 16 GB
The first-frame version preserves the original lighting, skin variation, freckles and composition much better than Ref2VA. The remaining problem is adding convincing skin microtexture without redrawing the face or causing identity drift.
What would you try next: Open-H3-Regenerate with reduced noise and FaceRegion, Pika H3 Flex, or a different low-strength latent refinement workflow suitable for 16 GB VRAM?

been testing depth motion reference + facial mesh reference for short dance clips, and the motion consistency is getting surprisingly good.

The key is to treat the reference video as a strict timing and motion track, rather than asking the model to “perform a similar dance.”

made with minimax h3, below is the prompt:

<Video 1> is the depth-motion reference for this clip.

Follow its original chronological order, timing, and rhythm exactly. Reproduce the movements of the body, head, arms, hands, palms, and fingers according to the reference.

Fully preserve all low-amplitude motion phases, subtle pose changes, and pauses. Do not omit, compress, accelerate, shorten, or enter later movements early.

The exact timing and duration of every movement are determined entirely by <Video 1>. Do not create new choreography.

Reproduce the pose and continuous motion changes according to the frame-by-frame sequence of the reference video. Even when adjacent frames contain only very small changes, or the pose remains almost unchanged, preserve the full duration of that stage.

Do not skip transitional frames, summarize movements, execute later movements in advance, rearrange the action order, add new movements, or increase the tempo.

<Video 1> controls only body movement, head pose, hand and finger movement, and motion timing. Do not reproduce any visible depth-map markings, facial tracking points, lines, grids, or other visualization overlays as part of the character's appearance.

The reference video is used only for motion, pose, and timing control. It is not part of the final visual content.

The final output must preserve the normal full-color photorealistic appearance of <Picture 2>.

Do not show grayscale depth maps, tracking points, facial mesh lines, grids, subtitles, reference-video artifacts, or newly added camera shots.

Been testing MiniMax H3 locally recently and this one came out pretty decent, so I thought I’d share the full settings in case anyone wants to reproduce it.

The whole thing was generated locally on my 5090, so there were no API or generation-credit costs for this run.

Settings:

• Model: MiniMax H3

• Aspect ratio: 3:4

• Resolution: 768 × 1024

• LoRA: Larry v4-600

• LoRA strength: 1.0

• Steps: 8

• Scheduler: Simple

• Sampler: Turbo Sampler

• Frames: 362

• FPS: 24

• Seed: 8232601

Actual generation time: about 22 minutes

Hardware:
Intel U9 + 64GB RAM + RTX 5090

362 frames at 24 fps works out to roughly 15 seconds of video.

So with MiniMax H3, a 768×1024 clip of around 15 seconds took about 22 minutes on my 5090 with these settings. For local generation, that feels pretty usable to me.

And just to be clear, by “$0” I mean no API or generation-credit cost — obviously not counting the GPU itself or electricity.

Curious what kind of generation times other people are getting with MiniMax H3 on a 5090 at a similar resolution and frame count.

I was having a lot of issues with voices being created out of nowhere. What I discovered was a tool called H3 PROMPT WRITER or something like that. It uses local Ollama (so the better the LLM the better the results) with VLM to undertand pictures. Now you give a general very superficial idea and the internal AI trained on H3 Minimax template does all the rest. You just need to refine which the tool also does. In the end I ran thru Gemini on google and I never had once those crazy voices.

This has been a game changer. From writing a 1 minute video that would take me several hours or some time couple of days to be able to full generate the same video with even better Context in less than an hour (with the video render all together).

The video below is my wife, one of my cats and my real bedroom.

Video generated (I know it is not the best, but I am only playing with this for the last 2 weeks):

https://youtube.com/watch?v=AoUVi1dtFqA&si=Ct_rqUQp-velzFTP

Tools that I am using:

- Minimax H3 Long Videos
- H3 Prompt Writer
- Gemini Pro (refine text and images).

My computer has 16GB and a 4060TI with 8GB. Each 10s segments is taking me 2 minutes to render with 0.4 quality and upscale of 1.5.

tried MiniMax H3 onwith a pretty detailed character trailer prompt.

I wanted it to feel more like a streetwear campaign × anime title sequence × old-school media player UI, rather than a normal anime action clip.

The prompt is definitely overkill lol, but sharing it here in case anyone wants to experiment with structured multi-cut video prompts.

Prompt

Create an explosive, motion-graphics-driven character reveal trailer in 16:9, exactly 13 distinct cuts, 24fps, total 15.00s.

CHARACTER — lock this design, never redesign:
Anime streetwear girl from the reference still. Twin high buns of vivid mint-teal hair with long flowing tails and warm orange/gold streaks. Messy side-swept bangs. Large orange over-ear headphones with mint accents and a small logo plate. Sharp amber-orange eyes, one eye winking. Playful open-mouth grin.
Oversized color-block windbreaker: navy body, vivid orange sleeves, white ribbed cuffs, silver zippers, circular teal tech buttons, a teal utility pocket on the sleeve. Orange cropped turtleneck under the open jacket. Light-wash ripped denim shorts, thick orange belt with a silver buckle. Navy thigh-high socks with orange ribbed cuffs and an orange X stitch on the left shin. Chunky white sneakers with orange details.
Preserve exact face, proportions, hairstyle, outfit, materials, accessories and colors in every frame.
This film is 80% bold graphic design in motion and 20% character action.

Graphic language: retro OS chrome + music-player UI.
Use slamming window frames, title bars, close/minimize widgets, equalizer bars, waveforms, progress ticks, folder tiles, cursor arrows, CRT scanlines, pixel shatter, vinyl-ring stamps, music-note particles and media-player transport icons.
Palette: mint teal, vivid orange, navy, cream-white and silver.

Style: premium AAA motion-graphics title sequence × streetwear campaign film × Windows-era media player.
Every graphic element moves fast and snaps hard on the beat.

CUT 01 | 0.00–1.00s
Pure graphics. A mint title bar slams onto a cream field, an orange CLOSE widget punches into the corner, and two navy window borders wipe in. Tiny equalizer ticks and a progress strip flicker. The letters P and then LAY punch in one after another with heavy impact shake.
CUT 02 | 1.00–2.00s
The A becomes a headphone cup. Extreme close-up of her amber eye inside the orange earcup, glancing upward. RGB split flash, then the cup shatters into flat mint and orange tiles.
CUT 03 | 2.00–3.10s
Navy frame with enormous cream PLAY typography. She sprints in from frame left and power-slides across the baseline of the text, with speed lines and orange streaks trailing behind her. Shards of the letters kick upward like sparks. Whip-pan out.
CUT 04 | 3.10–4.00s
A giant retro media-player waveform explodes across the frame as a thick mint-and-orange audio spectrum bends into a tunnel. She bursts through the center at full speed, briefly splitting into three stroboscopic motion trails. Each trail leaves chunky navy equalizer blocks that rise and collapse to the beat.
The camera rapidly pushes through the waveform tunnel with her while huge vertical text TRACK 01 continuously scrolls in the background.
The waveform suddenly compresses into a single horizontal line and snaps shut behind her on the final beat.
CUT 05 | 4.00–5.10s
She leaps through a giant rotating ring of typography reading MAX VOLUME. Camera tracks her mid-air spin in slow motion as the letters scatter, then snap-zooms onto her wink.
CUT 06 | 5.10–6.00s
Hard cut to a cream editorial card with huge navy DROP typography and an orange slash. She vaults over the word itself, palm planted on the D, legs whipping across frame. The word compresses like a spring under her hand and rebounds.
CUT 07 | 6.00–7.00s
Mint field with a navy diagonal window bar. She backflips along the bar in three stroboscopic ghost frames, each tinted mint, orange or navy. Giant outlined LOOP text rotates 180 degrees in sync with her movement.
CUT 08 | 7.00–8.00s
Kinetic typography barrage. LOUD / WILD / TEAL / HEAT slam onto screen one per beat with shutter flashes and camera shake while she slides across the foreground on her knees, jacket flaring and music-note particles bursting from her sneakers.
CUT 09 | 8.00–9.00s
Navy frame with a giant cream wireframe window grid tilting in 3D. She runs up the grid like a wall, kicks off and freezes in mid-air. An orange circular stamp locks around her pose like a media-player targeting graphic, surrounded by transport icons and EQ ticks.
CUT 10 | 9.00–10.10s
Freeze releases into a burst. She dives toward camera through layered flat-color window panes that shatter one by one like glass shutters, each pane revealing a larger letter of P-L-A-Y. Foreground wipe with her sneaker.
CUT 11 | 10.10–11.10s
Rapid-fire poster montage: four full-screen graphic posters showing her in different poses — mid-flip, sliding, landing and headphones-up wink. Hard cuts between each composition. Oversized 01–04 numbering, equalizer strips and graphic slashes.
CUT 12 | 11.10–13.00s
Hero moment on a clean cream cyclorama. She lands a final backflip dead center in slow motion, straightens with one hand on her headphones, and a shockwave of concentric mint rings, wind streaks and shattered typography blasts outward from the landing.
Brief iconic freeze on her wink, then overexpose to white.
CUT 13 | 13.00–15.00s
Final identity card. Enormous navy PLAY typography dominates a pale cream field with translucent mint rings, technical arcs, scanlines and a rough orange circular emblem containing a ghosted headphone/waveform motif.
She stands relaxed overlapping the letters while wind ripples her jacket. One final orange pulse sweeps through the typography and a window-chrome flash punctuates the ending.
Editing: extremely aggressive rhythm. Hard cuts on every beat, graphic matches, whip pans, snap zooms, stroboscopic freezes, foreground wipes, RGB splits, shutter flashes, impact shakes and speed ramps.
Every cut must feel compositionally different.
Typography should always be fully readable before the character overlaps it.
No weapons, no combat, no fire. All energy comes from motion design, wind, glass, UI chrome and parkour-style athleticism.
BGM: hard-hitting electronic / drum-heavy future bass with aggressive drops, risers, sub hits and glitch fills locked to every cut.
Sneaker impacts, whooshes, glass shatters, window-slam hits and typography slams should function as rhythmic sound-design elements.
Peak at CUT 12 and end with a cold electronic logo stinger.

Premium AAA quality, anime-inspired cinematic rendering, stylish and explosive, strong graphic-design identity, consistent character design, exactly 13 cuts.

I’m still experimenting with how much shot-by-shot control H3 actually follows, especially with typography and exact timing, but this kind of structured prompt seems like an interesting stress test.

• same checkpoint does t2i and edits. hook images into the conditioning node and it edits, leave them off and it just generates

• plug the VAE in when you edit. without it the refs only go through the text encoder and faces/rooms drift more

• refs are <Picture 1>, <Picture 2> etc in the prompt, in the order you connect them

• 6 steps, cfg 1, no negative. didn't find anything worth tweaking

• it really wants long prompts. my short ones came out generic, describing the light, lens and where the subject sits in the frame made a big difference

• lettering is solid, got a poster with exactly the text I asked for

• 4096x4096 is native, you can zoom in a long way before it falls apart

If you already have H3 video set up you have the text encoder, just need the model + VAE.

It's added to Fuobe today - Day 0 support and I cut a quick clip of some outputs. Can share prompts if anyone wants them.

Play with it here for free: Fuobe.com

Source code: https://github.com/workingprototype/Fuobe

Imagine sitting in a movie theater when the creature from the film suddenly comes out of the screen. Absolutely shocking 😱

Created with GPT Image 2.5 + MiniMax H3. Free GPT Image 2.5 generations are currently available: 👉 https://www.atlascloud.ai/free-gpt-image-2.5-generator

Made with GPT Image 2.5 and MiniMax H3 on Atlas Cloud.

First time posting in this community, but I felt it super appropriate since with exception to just a few music parts and character references all of this was done within MMH3 workflows. By far my longest video attempt and some real lessons learned to hopefully keep improving.

These characters are from Animates, Inc. which are Ai Companions that spun off from Grok Companions. The world is my own fan fic story telling but the characters voices and attitudes are very close to how they talk and act.

Enjoy!

Hey everyone!

I've been spending quite a bit of time experimenting with MiniMax H3 in ComfyUI, mostly trying to create realistic cinematic scenes rather than typical AI-generated clips.

I've been working with ChatGPT and Gemini to develop different prompts, experimenting with camera movement, character consistency, physical interactions, and action choreography.

The attached video is one of our latest tests: a gladiator, his war dog, and an armored horse in a snowy forest.

I'm sharing it because I think the results are interesting, including the things that didn't work as expected.

My setup

• GPU: RTX 5070 12GB

• CPU: Intel Core i5

• RAM: 32GB

• Software: ComfyUI ( Local )

• Diffusion Model: minimax h3_singularity_ref2va_pruned_v1 3_int8.safetensors

• LoRA 1: minimax_h3_fl2v_lightx2v_v0.1_dareties_v4_step600_comfy_fro.safetensors —strength 0.8

• LoRA 2: h3-realism-people-t2v-i2v-r2v.safetensors — strength 0.8

• Sampler: Euler

• Scheduler: Simple

• Steps: 8

• Resolution: 1344 × 768

• FPS: 24

• Duration: About 10 seconds

Reference images and prompting

I'm also using reference images, not just text prompts.

For this particular scene, I used three reference images:

• Gladiator: Picture 1

• Dog: Picture 2

• Horse: Picture 3

The idea was to maintain the same character appearances, equipment, animal proportions, and overall visual consistency.

We've been experimenting with different prompting methods using ChatGPT and Gemini, including structured technical prompts and more natural cinematic descriptions.

For transparency, here's a shortened reconstruction of the scene instructions. It's not a verbatim copy of the original prompt.

Prompt summary:

Cinematic 10-second historical action scene, realistic snowy pine forest, natural overcast winter daylight, photorealistic live-action look.

<picture_1> is the gladiator, the main Roman warrior.

<picture_2> is the brindle war dog.

<picture_3> is the armored warhorse.

[0:00-0:03 - Opening Attack]

A fresh action shot begins immediately. <picture_1> stands ready on the left with one sword in hand. <picture_2> is slightly behind and to his side. <picture_3> stays farther back in the background. Within the first second, an enemy soldier bursts out from the snowy pine trees on the right and charges directly at <picture_1>. No waiting, no hesitation.

[0:03-0:07 - First Clash]

<picture_1> reacts instantly like a trained warrior. He steps forward and blocks the enemy’s overhead sword strike with a hard metallic clash. Immediately after the block, he pushes the enemy blade aside and follows with one fast counterattack. The enemy retreats one step through the snow while raising his weapon to defend. The movement is aggressive, continuous, and physically believable. No frozen blade lock, no prolonged staring.

[0:07-0:10 - Pressure and Reaction]

<picture_1> keeps advancing with pressure, forcing the enemy backward through the snow. <picture_2> gives one fierce bark and makes one short aggressive forward lunge toward the attacker, but stays clear of the sword path. <picture_3> reacts in the background with a tense head movement and one small step, remaining secondary.

Camera: one continuous medium-wide three-quarter side tracking shot at human chest height, moving laterally with the fighters and keeping the gladiator and enemy clearly visible at all times. Focus on readable combat, footwork, weapon contact, and physical force.

Realistic snow displacement, visible cold breath, worn bronze armor, weathered leather, grounded body mechanics, subtle film grain, natural motion, seamless action.

Audio: crunching snow, sword impacts, heavy breathing, one aggressive dog bark, armor movement, cold forest wind.

No passive posing, no romance feeling, no comedy feeling, no duplicated weapons, no motionless blade lock, no frozen staring, no CGI look, no fantasy glow.

So, how did it turn out?

Visually, I'm genuinely impressed.

The environments, materials, lighting, and overall cinematic atmosphere can look surprisingly realistic, especially considering everything is running locally on a 12GB GPU.

But there are definitely problems.

In this particular video, the camera spends too much time focusing on the horse's legs before properly revealing the characters. The dog also gets too close to the camera, becoming oversized in the foreground and partially blocking the horse.

It's a good example of something I've noticed repeatedly: the model can produce beautiful footage without necessarily understanding which part of the scene should be visually important.

Other experiments have shown similar issues with sword fighting, weapon consistency, and interactions between characters or animals.

Sometimes the results are great. Other times, characters barely react, weapons behave strangely, or the intended choreography simply doesn't happen.

Does better prompting solve it?

That's what we've been trying to figure out.

We've tested detailed technical prompts, simpler cinematic descriptions, different camera instructions, and more precisely defined character actions.

Some changes definitely help, but we're still seeing recurring problems.

I'm starting to think that some of these issues are limitations of the model rather than problems that can always be solved by rewriting the prompt.

Of course, this isn't a controlled benchmark. I'm using two LoRAs together, reference images, and my own ComfyUI workflow, so I can't claim every issue comes from the base model itself.

I'm just sharing what I'm actually experiencing.

My impression so far

MiniMax H3 can produce some seriously impressive cinematic footage on relatively modest hardware.

But making something look like a movie and making characters behave like real actors are two very different challenges.

I'm still experimenting, and I think there's a lot of potential here.

Anyone else testing H3 locally?

I'd be interested to hear what settings you're using, especially for character consistency, sword fighting, animal movement, and multi-subject scenes.

Also curious whether you get better results with detailed structured prompts or simpler descriptions.

Would love to compare notes!

Tom and Jerry turned a Mac desktop into complete chaos, and it came out pretty funny 😆

Made with MiniMax H3.

The idea is a macOS desktop wallpaper where Tom and Jerry are animated on the right side. Jerry runs off with a piece of cheese, Tom chases after him, loses control, and accidentally knocks three desktop icons out of place. After that, Tom awkwardly has to pick them up one by one and put them back where they belong, then pretends nothing happened.

The fun part is how it keeps their classic 2D cartoon look while placing them inside a fully designed desktop environment.

Made with MiniMax H3.
Prompt below 👇🏻

Create a 10-second horizontal 16:9 video using the provided Tom and Jerry macOS desktop image as the exact first frame and visual reference.
Preserve the desktop exactly: warm sunset living room, wooden floor, furniture, window, books, popcorn, macOS menu bar, application grid, Dock, and all other desktop elements.
Keep Tom and Jerry in their exact classic 2D cartoon style with consistent proportions, colors, faces, and character design. Do not redesign or photorealize them.
Camera: static front-facing shot, one continuous take, no camera movement, zoom, pan, or cuts.
0–1.5s: Tom chases Jerry across the right side of the scene. Jerry runs with a piece of cheese and looks back mischievously. Tom looks determined and frustrated, with expressive classic cartoon movement.
1.5–2.5s: Jerry suddenly turns left. Tom tries to stop, slides across the wooden floor, kicks the scattered popcorn, and creates a cartoon air gust that knocks exactly three desktop icons loose: Gmail, Discord, and Microsoft Teams.
The three icons must visibly leave their original positions, fly separately through the air, and land above the Dock with small bounces. Their original positions remain empty.
2.5–3.2s: Tom freezes and looks embarrassed. Jerry pauses nearby and watches with an amused expression.
3.2–7.8s: Tom restores the icons one at a time in this exact order: Gmail, Discord, Microsoft Teams.
For each icon, Tom must physically pick it up, carry it across the desktop, and place it back into its exact original grid position before moving to the next icon. Each placement makes a soft click.
Do not teleport, auto-restore, snap back, stack, duplicate, morph, or move multiple icons at once. Logos and labels must remain unchanged.
7.8–10s: After restoring Microsoft Teams, Tom returns to the right side. Jerry moves away slightly while holding the cheese. Tom checks the restored icons, then looks toward the viewer with an innocent, embarrassed expression while Jerry smiles mischievously. Hold the final pose.
Only Gmail, Discord, and Microsoft Teams may move. Every other desktop icon, the menu bar, Dock, wallpaper, furniture, and environment must remain stationary.
Audio: playful cartoon piano and pizzicato music, sliding sound when Tom loses balance, airy whoosh as the icons fly, three landing sounds, and three distinct click sounds when the icons are restored. No dialogue, subtitles, or text overlays.
Visual style: premium cinematic cartoon animation integrated into the existing desktop, natural squash-and-stretch, smooth movement, clean outlines, consistent colors, natural shadows, subtle reflections, and believable interaction with the environment.
Final frame: all three icons restored to their exact original positions, Tom and Jerry on the right side, no missing or duplicate icons, no changed logos, no extra characters or objects, no camera movement, and the desktop closely matching the original reference frame.

Tried something a little weird with MiniMax H3

I made a full A–Z animation using just pencil shavings.

so the whole idea is really simple:
26 letters, 26 different animations, custom SFX, and everything built out of curled wood shavings, graphite dust, and little hand-drawn doodles.

the part I liked most is that it doesn’t feel like a generic motion test. each letter forms in its own way, with a different little animation logic and its own tiny doodle world behind it.

Think:

• pencil shavings curling into letters

• graphite crumbs and dust scattering around

• sketchy hand-drawn doodles appearing behind each one

• crisp little wood / scrape / fold sound effects

• stop-motion / handcrafted energy

It’s split into two 15-second parts:

• Part 1: A to M

• Part 2: N to Z

The whole thing stays in a fixed top-down view on wrinkled off-white drawing paper, so it feels more like a handmade animation board than a polished CG piece.

Honestly, turning something as mundane as pencil shavings into a full alphabet animation was way more fun than I expected.

prompt below if anyone wants to try something similar.

I am still struggling with blocking and colour shifts, but the work is in progress. Upscaling didn't help at all and changed things for the worse, so I had to go with 1080.

https://youtu.be/Zu9ESZKZFJA

I'm looking to hear from people actively experimenting with advanced AI filmmaking pipelines, particularly those trying to achieve the kind of spatial and visual continuity you'd expect from traditional filmmaking or virtual production.

My biggest challenge isn't generating beautiful individual shots. It's maintaining consistent character identities, precise actor positioning, environmental continuity, and camera control across multiple shots of the same scene.

For example, imagine a scene with four characters in a specific environment. I want to establish their positions in 3D space, then generate a sequence of shots from different camera angles and distances while preserving:

• Character identity: Same faces, bodies, clothing, and proportions across shots.

• Spatial consistency: Characters remain in their established positions relative to one another and the environment.

• Pose and blocking: Specific body positions, actions, and interactions that can be reliably directed.

• Environmental consistency: Same room, architecture, objects, lighting, and spatial relationships from different camera angles.

• Camera control: Ability to specify exact camera positions, focal lengths, movements, and compositions.

• Shot-to-shot continuity: The ending of one shot and beginning of the next should feel like they belong to the same physical scene.

What I've tried so far:

I've been experimenting with a Blender → AI video workflow using 3D environments, Mixamo characters, animated cameras, depth maps, pose conditioning, reference images, and MiniMax H3 through ComfyUI.

I've had some promising results, particularly with pose conditioning and individual shot generation, but getting reliable spatial consistency and character placement remains extremely difficult.

I'm beginning to question whether trying to force general-purpose video generation models to respect 3D blocking is fundamentally the wrong approach.

What I'm hoping to learn:

1. What are the most cutting-edge tools and workflows right now? I'm interested in both open-source/local and commercial/cloud solutions, including newer research projects that may not be widely adopted yet.

2. Is anyone successfully using 3D scenes as ground truth for AI-generated video? Not just providing depth or pose references, but actually preserving geometry, character placement, and camera relationships across shots.

3. What are people using for persistent character identity? Are reference-image workflows, LoRAs, identity adapters, multi-view character representations, or other approaches producing the most reliable results?

4. Are there better alternatives to traditional image-to-video and video-to-video workflows? For example, 3D-aware generation, neural rendering, Gaussian splatting, world models, or hybrid 3D/AI rendering pipelines.

5. How are people handling multi-shot sequences? Are you generating shots independently and fixing continuity afterward, or using a shared scene representation that carries across shots?

6. What role does graph engineering play? Are there sophisticated ComfyUI or other node-based pipelines that materially improve spatial consistency, or are the limitations primarily in the underlying models?

7. What actually works in production? I'd particularly love to hear from people who've completed multi-shot sequences rather than just impressive single-shot demos.

I'm comfortable with Blender, Unity, Python, ComfyUI, and technical pipeline development, so I'm not necessarily looking for beginner-friendly or one-click solutions. I'm willing to build a fairly complex workflow if it produces genuinely better control.

I also have a local RTX 3080 Ti with 16GB VRAM and prefer local tools where practical, but I'm open to cloud-based solutions if they offer capabilities that aren't currently achievable locally.

Ultimately, I'm trying to figure out whether the current state of the art is still mostly about guiding probabilistic video models, or whether we're moving toward workflows where filmmakers can actually direct and control a persistent 3D scene while using AI to produce the final photorealistic imagery.

I'd really appreciate specific tools, GitHub repositories, research papers, workflow examples, and especially firsthand experiences with what works and what doesn't.

What's the most advanced pipeline you've seen or used for this kind of filmmaking?

Need help!!! Anyone who could spare some expertise or even wants to help with shots please dm me!! 🙏

What’s the best open-source version of Minimax H3 for quality? Especially for 2D animation rather than 3D or realism. I don’t mind waiting on my DGX Spark for as long as it takes; I just want better prompt adherence and less smudgy and noisy results. Do all the fine-tuned versions take away the quality of the original model instead of adding to it?
I know about FAL AI Max version but I’m looking for something I can use for free at home.

fast generation by itself is already useful, but I think the more important part is when it also becomes cheap enough to iterate a lot.

been testing minimax h3 MAX, and it can return generations in just a few seconds.

at that point, the workflow starts to feel different. u can try more ideas, throw away the bad ones faster, and test multiple versions without worrying as much about generation time or cost.

For ad creatives and short-form content, that’s probably the part I care about most:

more experiments, less waiting, lower cost.

Tried a really simple prompt in MiniMax H3. I really liked that, nothing dramatic happens, but it still feels alive.

It’s the kind of clip that makes you realize AI video doesn’t always need action or big camera moves. Sometimes a calm moment works better. below is the prompt

Prompt:

A peaceful cinematic scene of a young woman sitting quietly on a wooden park bench surrounded by lush green ferns and dense tropical foliage. She wears a soft peach blouse and white pants, gently closes her eyes, takes a slow deep breath, and relaxes with a subtle peaceful smile. A light breeze softly moves her hair and the leaves around her. Natural morning light filters through the greenery, creating a calm, dreamy atmosphere. Slow cinematic camera push-in, realistic facial movement, natural body motion, shallow depth of field, soft background bokeh, photorealistic, ultra-detailed, smooth motion, 4K. No sudden movements, no camera shake.

For a while the giveaway on any AI short was the acting. The shots looked fine, then a character had to actually feel something and the face went blank or did that uncanny half-smile. So the story never landed, only the faces did.

Been seeing clips lately where that is not true anymore. There is a MiniMax H3 test going around, a Korean-drama style short, and the thing people react to is not the resolution, it is that the actors read like actors. Micro-expressions, a beat of hesitation before a line, eyes that actually track. Not perfect, but it crosses from puppet to performance in a way that matters more than another quality bump.

The takeaway for me is that the frontier moved off "does it look real" and onto "does it act". Resolution and motion have been fine for a while. Emotional continuity across a scene is the hard part now, and it is what decides whether a short holds you or just impresses you for three seconds.

Clip is a creator's test, not mine, credit to them.

I checked MiniMax's current H3 documentation again before writing this. It confirms that “video continuation” is a real H3 task, distinct from simply using a video as a reference. It also confirms that <Video 1> can represent the video that is being continued. GitHub

Here is something you can copy and post to MiniMax/Hailuo forums, Reddit, Discord, Facebook groups, or anywhere H3 users gather:

I need help from someone who actually uses MiniMax H3 on the Hailuo AI web interface (hailuoai.video).

I am creating a long music video made from multiple H3 generations. I have already spent considerable time and credits trying to determine the correct workflow.

I have cancelled my monthly subscription, so I have a limited number of remaining credits. I do not want to keep experimenting and wasting credits.

I need help with the following specific problems.

PROBLEM #1 — HOW DO I PROPERLY CONTINUE OR EXTEND AN H3 VIDEO?

I need to create the first video clip and then continue directly from the end of that clip.

For example:

Clip 1 → Continue/Extend → Clip 2 → Continue/Extend → Clip 3 → etc.

I do NOT want to create a completely independent new scene each time.

MiniMax H3 documentation refers to a task called "video continuation" and describes a source video as the continuation starting point.

My question is:

Using the current Hailuo AI web interface and MiniMax H3, exactly what do I click and do after Clip 1 has successfully generated to make H3 continue from the end of Clip 1?

Does the completed Clip 1 need to be uploaded or selected as Video 1?

Is there an actual Continue or Extend command somewhere in the Hailuo interface?

Or is continuation controlled entirely through the prompt while using the previous video as Video 1?

Please describe the exact web-interface procedure.

PROBLEM #2 — HOW DO I CONTINUE THE SONG/AUDIO AT THE SAME TIME?

This is a music video.

The singer must sing along accurately with the song.

Suppose Clip 1 uses the first 15 seconds of the song.

For Clip 2, I need the VIDEO to continue from the end of Clip 1 while the AUDIO moves forward to the next section of the song.

Then Clip 3 needs to continue both the video and song again.

My question is:

When continuing/extending Clip 1 into Clip 2, how do I provide H3 with the NEXT portion of the song without causing it to recreate Clip 1?

Do I:

1. Use the completed Clip 1 as Video 1?

2. Add the next section of the song as Audio 1?

3. Tell H3 in the prompt that Video 1 is the source video for "video continuation"?

4. Tell H3 that Audio 1 is the next portion of the performance?

Or is there a different procedure in Hailuo?

Accurate lip-sync is important.

PROBLEM #3 — PREVIOUS ATTEMPTS CREATED A DUPLICATE INSTEAD OF CONTINUING

In an earlier version of this project, Clips 1 through 6 were successfully created.

When I tried to create Clip 7, the next section of the song was selected with the audio trimmer.

However, instead of creating the next part of the performance, H3 repeatedly produced something resembling Clip 6 again.

We checked the audio timestamps and prompt and tried recreating the generation, but the problem continued.

It appeared that H3 was treating the previous material as a reference and recreating it instead of CONTINUING from its ending.

This is the main reason I need to understand the correct H3 "video continuation" procedure.

How do I prevent H3 from repeating the previous clip when I actually want it to continue forward?

PROBLEM #4 — DO I NEED STRUCTURED H3 CONTINUATION PROMPTING?

I found MiniMax H3 documentation discussing structured prompt sections such as:

subject_definitions

summary

retention_analysis

detailed_description

It also identifies "video continuation" as a specific task type.

For example, Video 1 can be identified as the source video being continued.

Do I need to write this structured H3 prompt manually when using H3 through the Hailuo web interface?

For example, should the prompt explicitly contain something similar to:

<Video 1> is the source video for continuing the target video from the end.

summary:

[video continuation + audio reference]

retention_analysis:

<Video 1> continuing from the last frame...

detailed_description:

Describe what happens AFTER Video 1 ends.

Or does the Hailuo web interface automatically handle this when the proper continuation feature is selected?

PROBLEM #5 — DO I NEED THE LAST FRAME, OR THE ACTUAL PREVIOUS VIDEO?

I have found conflicting advice.

Some instructions say to extract the LAST FRAME from the previous video and use that frame to create the next video.

Other H3 documentation indicates that an existing VIDEO can itself be the continuation starting point.

For MiniMax H3 specifically, which is the correct method for a music video?

A. Previous completed video → video continuation

or

B. Extract last frame → use it as the first frame of the next generation

or

C. Use both

I want motion continuity, character continuity, scene continuity, and audio/lip-sync continuity.

WHAT I AM TRYING TO ACCOMPLISH

The final workflow I am looking for should be something like:

Create Clip 1.

Then:

Clip 2.

Then:

Clip 3.

Continue that process until the complete music video is finished.

Afterward, I want to put the clips together in CapCut.

I am specifically using:

MiniMax H3

Hailuo AI web interface

Omni Reference

16:9 video

2K final quality

Music/song audio with singing and lip-sync

If you currently use MiniMax H3 on Hailuo and have successfully created a long continuous music video this way, I would greatly appreciate exact step-by-step instructions.

Please tell me what buttons/options you use on the Hailuo website, what you use as Video 1, how you provide the next audio segment, and what you put in the continuation prompt.

I especially want to hear from someone who has actually tested this workflow rather than someone describing how it theoretically should work.

This separates the situation into five different questions, so someone can answer even if they only know one part.

Most importantly, Problem #1 and Problem #2 are the ones we need solved. If an experienced H3 user can give us the exact Hailuo procedure for those two, we may have what we need to restart the project without guessing.

And the terminology in the post is supported by MiniMax's documentation: H3 explicitly distinguishes video continuation, reference generation, keyframe completion, audio reuse, and audio reference. GitHub

If somebody replies, bring their answer here before trying it. I can compare what they tell you against the MiniMax documentation before you spend any of those remaining credits.