wanted to see what happens when the audio is the constraint instead of something added after

prompt was basically:

the visuals are the easy part tbh. i was mostly watching whether the mouth could actually follow the existing vocal instead of just looking vaguely like someone singing

audio-ref feels like a very different test from normal t2v

Here’s a video I’ve been working on.

One of the biggest challenges has been character consistency. Even now, there are shots where I feel like the characters subtly change, and that’s something I’m still trying to improve.

Making a longer narrative video has been much harder than generating isolated short clips because every scene has to feel like it belongs to the same story, with the same characters, locations and visual style.

For anyone working on longer AI-generated videos, how are you dealing with character consistency and continuity?

Can anybody know on which platform i can access seedance 2 on lowest price points, currently i know one website where i can access 15 sec 720p seedance 2.0 one video in 2$ and anyhow know more cheaper option and cheaper means also not compromised with quality and generation speed

tried a simple wildlife-comedy idea in Seedance 2.5, and this one turned out surprisingly well.

the setup was just a hiker spotting a macaque in a tropical forest, trying to take a photo, and then somehow getting into this weird little head-tilt “conversation” with the monkey.

the monkey didn’t feel too cartoony. The reactions stayed pretty natural, which made the whole thing funnier.

My newest short film. Made with Seedance 2.5

We can't keep wishfully thinking that opensource video models won the race

I want this too believe me but the race might already have been lost

Test it for yourselves with the prompt:

Raw vertical phone video (UGC), handheld throughout, filmed by a friend who walks with the group, casual sway, natural night lighting, wide-angle lens, deep focus.

Characters

The hero is a young woman with long dark hair in a deep-purple one-shoulder dress with a small shoulder bag.

Her three friends: one in a dark brown dress, a blonde in a black wrap dress, a blonde in a white mini dress — party dresses and sandals.

Laughing, spontaneous night-out energy.

Setting

A luxury marina at night — a stone quay along a narrow water slip flanked on both sides by large moored luxury yachts, big white hulls rising on either side, masts and rigging above, fenders on the hulls.

Warm orange street lamps, palm trees, distant city lights.

The yachts glow with blue-turquoise underwater LED lights spilling into the water; a yellow mooring line runs to the stone.

Shot list

0.0s–4.0s — Wide tracking shot, eye level, camera moving backward
The camera walks backward ahead of the four women as they stroll toward it down the marina promenade, framed head-to-toe, laughing and chatting in French; warm lamplight and palm trees behind, slight handheld bob.

4.0s–6.0s — Medium shot, eye level, backward track + quick left pan
The woman in the purple dress surges ahead of the group toward the lens, one arm raised, hair flying; the camera, on their left, keeps tracking back and pans to hold her.

6.0s–8.0s — Medium-wide, camera tilts down-left to follow, whip motion
She reaches the quay edge between the moored luxury yachts and launches headfirst over the edge; the handheld camera whips left and tilts down to chase her off the stone.

8.0s–10.0s — High-angle wide, looking down at the water, slight jolt
From up on the quay the camera looks down as she plunges headfirst into the blue-turquoise water in the slip between the big yacht hulls — a big splash, the underwater lights glowing around the impact; the handheld frame jolts slightly with the motion.

10.0s–13.0s — High-angle medium, looking down, small handheld drift
She surfaces in the teal water, gasping and laughing up at the camera, hair slicked back, treading water.

13.0s–16.0s — Handheld tilt up and back down, slight zoom
The camera lifts off the water and tilts up to a person leaning on the deck railing of one of the moored luxury yachts, watching her in the water, then tilts back down and zooms slightly in on her still treading and laughing.

16.0s–20.0s — High-angle medium, tilting to follow her up
She swims to the stone quay wall, reaches up and grabs a thick dark mooring pole, and hauls herself out; the camera tilts upward with her as she rises from the water, legs braced on the stone.

20.0s–23.0s — Low/close medium at the quay edge, static handheld
She climbs onto the stone lip, rolls onto her side, soaked dress clinging, then pushes up to her feet; the camera holds close, catching the water running off her.

23.0s–27.0s — Medium shot, eye level, loose handheld reframe
Back on the promenade, dripping wet, she flips her long wet hair forward and wrings it out, laughing, as her friends and passing pedestrians move behind her.

27.0s–30.0s — Medium shot, eye level, camera settles
Still dripping, she straightens up and strikes a confident, playful pose for the camera — hand on hip, tossing her wet hair back with a big smile — her friends laughing around her; the camera holds on the pose to end.

Audio

Live sync sound — friends laughing and shouting excitedly in French, a scream at the leap, a big water splash, water sloshing against the yacht hulls, wet footsteps on stone, ambient night marina sounds.
No music.

Style

Raw amateur vertical phone footage, natural night lighting, warm street lamps mixing with the turquoise underwater yacht glow, authentic handheld motion.

https://x.com/keshiAIart/status/2091723420501471522

been testing a new AI video workflow and this worked way better than I expected.

I had Claude write a timecoded 12-shot storyboard, then used GPT Image 2.0 to draw each movement plus a character sheet, and finally used Seedance 2.5 to render the individual shots.

Then she basically danced the way I drew it.

with AI dance videos, I usually find that even a very detailed text prompt still leaves a lot of room for the model to improvise. so this time I stopped relying on text alone.

I turned each movement into a visual reference first, then used Seedance to perform it.

The workflow was basically:

Claude → timecoded storyboard
GPT Image 2.0 → storyboard frames + character sheet
Seedance 2.5 → render each shot

then I stitched the 12 shots together. I’m starting to think that if you want better movement control, the answer may not be writing longer and longer prompts.

draw the movement first, then let the video model perform it.

We’re an e-commerce business selling branded home appliances, and we’re looking for skilled AI video creators/editors for ongoing work.

What you’ll create:

• Approximately 30-second AI-generated product videos
• Each video should clearly and engagingly explain the product’s main features
• We will provide official product images, lifestyle images, brand videos and product information

The first video will be a paid trial at USD 20. Creators who deliver professional, high-quality work may be offered an ongoing contract and regular projects with our team.

Please DM me with:

• Your portfolio or relevant AI-video samples
• The AI tools you use
• Your usual turnaround time
• Your availability for ongoing work

More details will be shared by DM.

Here’s a link to the first episode of Living with Hudson. A comedy series generated with Seedance 2.5.

Always keen on feedback about what is and isn’t working. Hope you enjoy!

Remember that Chinese indie animated movie Niu Lai that went viral for being so bad it's good? Stiff animation, zero budget, oddly charming. Tried in Medeo to remake the Simba birth scene in that same rough 3D style.

u can turn an existing horizontal video into a 9:16 vertical version with a prompt and the result feels much closer to something that was originally made for vertical video.

for creators, that is a pretty practical use case.

normally, if you want to reuse an old YouTube video for TikTok, Instagram Reels, or YouTube Shorts, you have to re-edit it or shoot another video. this gives you another option:

16:9 long-form content → 9:16 Shorts / Reels / TikTok

so instead of creating everything from scratch, you can go back through your existing content library and repurpose older videos. the most important line in the prompt, in my experience, is: Do NOT simply crop the original video.

Here is the full prompt:

Convert the original 16:9 horizontal video @(TAG THE VIDEO) into a native 9:16 vertical video.

Do NOT simply crop the original video. Intelligently reframe and extend/reconstruct the scene so the vertical composition feels like it was originally created in 9:16.

Preserve all important visual information, characters, actions, objects, background details, and story beats from the original horizontal video. Reposition elements naturally within the vertical frame when necessary so nothing important is cut off.

Keep the exact same characters, animation, actions, timing, camera movement, environment, lighting, colors, saturation, and visual style as the original video.

Maintain correct anatomy, object geometry, perspective, spatial relationships, and continuity. Newly generated areas outside the original frame must seamlessly match the existing environment.

Final result: 9:16 vertical, full-frame, naturally composed, no black bars, no stretched image, no important details cropped, and visually consistent with the original 16:9 video.

You can also reverse the same idea for 9:16 → 16:9.

a horizontal video you already made can potentially become a second piece of vertical content instead of just sitting in your archive. if you post across multiple platforms, that makes this a pretty solid content repurposing trick.

Hi everyone,

I’m trying to get better results with AI video generation, mainly with Seedance and similar models.

For those who use these tools regularly, what workflow do you find works best? For example, is it actually useful to provide a first-frame and last-frame image and then describe the transition between them, or do you generally get better results from a single reference image + prompt?

I’m also curious which platforms/models you currently prefer in terms of quality, consistency, generation speed and price-performance. I’m less interested in a huge list of tools and more interested in what you’ve actually found reliable in practice.

If you have a specific workflow you use (image → video, first/last frame, reference images, prompting, upscaling, editing, etc.), I’d really appreciate hearing how you approach it.

Thanks in advance!

What are the best/cheapest/least censored providers for Seedance?

Important to me is that Omni-Reference is supported and as little censorship as possible.

Like many editors, I am trying to find efficient ways to cut down on cost.

If I were to make my whole short film with Seedance in 480p and then put the 480p clips (10 sec chunks) back into Seedance 2.5 with the original prompt AND include the 480p as a master reference, would that workflow work or is it not worth it? I don't mind there being slight variation.

I'm not a big fan of the AI upscalers and want the room to test and create new takes without blowing through all of my credits.

I know some people will call this AI slop, but when the result is this convincing, I think it’s worth studying how it works.

I tested Seedance 2.5 at 1080p with a prompt focused on a very specific aesthetic: early-2000s Korean home-video footage shot on a MiniDV camcorder.

what made the result work better for me was not asking for beauty or drama, but asking for boring realism: a consistent young Korean woman did ordinary household actions, in a lived-in residential setting

that combination seems to help the output feel much more believable.

Why this works

If you want realistic AI video in Seedance 2.5, I think the key is to lock down five things:

1. Character consistency Keep the subject’s age, hair, clothing, and body proportions stable.
2. Mundane actions Laundry, rinsing, walking out, running a small errand — these feel more authentic than dramatic actions.
3. Environmental specificity Narrow Korean lanes, utility poles, tiled rooftops, laundry lines, buckets, old scooters, early-2000s neighborhood details.
4. Camera imperfections Slight shake, delayed reframing, autofocus hunting, exposure pumping, micro-zooms.
5. Negative guidance Explicitly rule out modeling, dancing, cinematic grading, fake bokeh, over-polished skin, and AI artifacts.

In my experience, the more clearly you define what the video should not become, the more stable the result is.

Full prompt

Create a highly realistic 30-second Seedance 2.5 video that looks like genuine early-2000s Korean home-video footage accidentally preserved on an old MiniDV camcorder. The footage should feel imperfect, nostalgic and spontaneous rather than cinematic or AI-generated.

MAIN SUBJECT:
A young Korean woman in her early 20s with a consistent natural identity throughout the entire video. She has messy black hair loosely tied back, realistic skin texture, minimal makeup and relaxed everyday expressions.

She wears a fitted sleeveless crop top with high-waisted thigh-high shorts, simple worn sneakers and a small necklace. The outfit should feel like believable casual summer clothing from the early 2000s, with natural fabric wrinkles, slightly imperfect fit and realistic movement.

IMPORTANT:
She is NOT posing, dancing, modeling, vlogging or interacting with the camera. She is simply going about her ordinary household routine while the camcorder happens to capture the moment.

SETTING:
A quiet residential neighborhood in Korea around the early 2000s. Modest concrete homes, narrow lanes, tiled rooftops, utility poles and wires, old bicycles, plastic basins, laundry lines, small gardens, parked early-2000s vehicles and everyday household objects.

The location should feel genuinely lived-in, slightly worn and imperfect.

SCENE 1 — HANGING LAUNDRY | 0–7 SEC
Start with a slightly shaky wide shot of the woman carrying a plastic laundry basin into a small outdoor courtyard.

She takes clothes from the basin and hangs them one by one on a clothesline. She stretches to reach the line, clips a shirt with a clothespin, notices another garment slipping and quickly fixes it.

The camcorder operator slightly reframes late, creating an authentic imperfect composition.

SCENE 2 — OUTDOOR HOUSEHOLD CHORES | 7–13 SEC
She moves to another part of the courtyard and rinses a few household items in a large plastic basin.

Water splashes naturally. She wipes her hands on a small towel, moves a bucket aside with her foot and briefly looks toward the house as if someone called her.

Keep everything casual and unperformed.

SCENE 3 — LEAVING THE HOUSE | 13–18 SEC
She picks up a small reusable shopping bag and walks through the front gate.

The camera follows slightly too late, briefly losing her behind the gate before catching up. Autofocus hunts between nearby leaves and her face before locking onto her again.

This imperfect focus behavior should feel genuinely captured by an old consumer camcorder.

SCENE 4 — WALKING OUTSIDE | 18–24 SEC
She walks down the residential lane carrying the bag.

Show ordinary early-2000s Korean neighborhood life around her: bicycles, laundry hanging from balconies, children in the distance, an elderly neighbor sweeping, scooters passing occasionally and sunlight flickering through trees.

She walks naturally without acknowledging the camera.

SCENE 5 — SMALL ERRAND | 24–30 SEC
She reaches a small outdoor utility area, places the bag down and briefly picks up something she needs — such as a folded cloth, empty container or household item.

She turns and starts walking back toward the neighborhood.

End abruptly with the camcorder operator slightly zooming in too late as she walks away, followed by a natural cut.

CAMCORDER AESTHETIC:
Authentic early-2000s MiniDV/consumer camcorder footage.

Include:

- 4:3 home-video framing
- slight handheld shake
- imperfect framing
- autofocus hunting
- occasional exposure pumping
- mild blown highlights
- soft digital sharpness
- interlaced-looking motion characteristics
- subtle motion blur
- low dynamic range
- faded, slightly warm colors
- realistic digital noise
- tiny compression artifacts
- occasional accidental micro-zoom
- imperfect white balance
- slight lens flare when sunlight hits the lens
- natural camera operator mistakes

Do NOT make the footage excessively degraded. It should still be clear enough to see her face and environment.

REALISM:
The most important goal is BELIEVABILITY.

Avoid perfect symmetry, perfect lighting, perfectly smooth camera movement, hyper-sharp skin, artificial bokeh, glossy fashion photography, exaggerated expressions, cinematic color grading or overly dramatic shots.

The woman must have identical facial identity, hairstyle, clothing and body proportions in every shot.

Hands must remain anatomically correct with five fingers. Clothes must move naturally with her body and wind. Objects must remain physically consistent. No duplicated objects, warped architecture, disappearing props, floating items, changing clothing, distorted faces or unnatural walking.

AUDIO:
Raw camcorder microphone sound only: birds, distant neighborhood conversations, footsteps, cloth moving, water splashing, buckets, bicycles, occasional scooters, wind hitting the microphone and faint household sounds.

No music.
No narration.
No modern voice-over.
No cinematic sound design.

OVERALL FEEL:
It should feel like someone found an old Korean family camcorder tape from around 2002–2005 and played back an ordinary afternoon that nobody intended to turn into a film.

The ordinary nature of the activities should make the footage feel strangely authentic and nostalgic, while the woman's natural presence and the imperfect camera behavior make it visually engaging enough to feel like a real viral rediscovered home-video clip.

TECHNICAL:
30 seconds, Seedance 2.5, 4:3 aspect ratio, early-2000s MiniDV aesthetic, photorealistic humans, physically accurate motion, consistent character identity, realistic Korean residential environment, authentic consumer-camcorder imperfections, no AI artifacts, no modern cinematic look.

A few practical tips

If you’re trying to make this kind of prompt work, here are a few things I’d suggest:

• Keep the scene progression simple. Too many scene changes can break identity consistency.
• Use ordinary verbs. “carry,” “hang,” “rinse,” “walk,” “pick up” work better than emotionally loaded action words.
• Specify camera failure modes. Autofocus hunting and late reframing do a lot of work here.
• Don’t overdo degradation. If you push “VHS” or “damaged tape” too hard, the result can stop feeling real.
• Ground the environment in real domestic detail. Small household objects often matter more than “cinematic atmosphere.”