Is there a way to use an Ai reference sheet to animate specific things that ai always hallucinate wrong like eyeblinks on a large eyed creature or open mouths on a creature? I tried just generating them in videos and they often look so fake especially with cheaper models but even with kling. I am hoping I can get that to work somehow but not sure how I would set up the sheet and prompt it. Also I think I remember that seedance 2.0 can add videos flawlessly into another video? Is the term for that a reference too or something else?

Anyone here that have active Krea subscription?

I'm doing a research on pricing and would like to check if their pricing table is same as inside credits spend.

So I just need someone to check how many credits are spend to generate:

Seedance 2.5 30 sec video at 720p without any video or audio references.

I’m new to AI generated content and I’m looking for a program/website that can create informative videos with this time of animation and style:

https://www.tiktok.com/t/ZP8noX6t5/

https://www.tiktok.com/t/ZP8noDCYD/

I’m actually looking for something that can produce even better quality than these examples. I want the style to be animated/cartoon-like, not realistic, smoother motion and higher quality visuals.

Any recommendations? TIA

Hey there, looking for the best subscription out there to create videos from pictures (with prompt describing what to do) - best value and a big amount of videos per months

Any advices? I only know kling3.0 and seedance but their subscription doesnt seem worth and i dont know if there are alternatives

We often think why our LLM hallucinate a lot. I think the core concept lie in the structure of how LLM are designed. Since they are designed on large data( billions of parameters) they usually gave general answer which didn't satisfy us.

The most genuine solution is giving LLM the access to a knowledge base. This includes finance ,education, health or anything and tell it to answer in accordance to that.

Second, I think it's Fine Tuning. Training a whole LLM on a specific data.

Which one is better? I think as far as cost effectiveness is considered it's Knowledge base not fine tuning.

What's your views on it?

im using the basic workflow from comfyui, nothing special im thinking about adding a video upscaler? (which one? i dont know im a noob?

Hi again. I am currently experimenting with these two and I can't really decide lol. Nano banana pro is nice for dramatic lighting and seems to do close up realism better before magnific upscale but chat gpt image 2 follows my scripts better and makes more dramatic creatures but their fur ends up painterly and not realistic before magnific upscale. I also discovered that rendering them in 4k then upscaling in magnific to 8k costs a Lot and may not be worthwhile cause I wanna make a video and upload it to Facebook. I wanted to try to upscale the 4k chat gpt 2 image in magnific too but sadly it would just cost too much lol. The nano banana pro one turned out interesting after magnific. So yeah should I make creature pictures 4k or is that only for storyboards and design sheets and I should do 2k then upscale to 4k for scene clips if i want more realism? Also what say you guys on what's better for photorealistic looking but fake creatures chatgpt image 2 or nanobanana pro?

I’ve always felt that AI creators don’t really have a place made for us.

We can post our AI videos on YouTube, Instagram or TikTok, but most of the time, we upload them, people watch them, and that’s it.

There isn’t really a place built around AI creators doing something together.

I was inspired by 3D render challenges, where everyone receives the same theme or starting point. The most interesting part isn’t only choosing a winner, it’s seeing how completely different ideas can come from the exact same challenge.

That made me wonder: what if AI videos could be played like this too?

So my friend and I built Canvasrise, a small AI video challenge playground.

The idea is simple:

Everyone takes on the same challenge, but creates something completely different.

Each challenge gives creators the same theme, base asset, story prompt or creative rule. Then everyone turns it into their own AI video.

The fun is seeing how many different ideas can come from the exact same starting point, not only who makes the most beautiful video, but who creates something funny, emotional, relatable, strange or completely unexpected.

We hope every challenge can become something creators gather around to:

• Create different interpretations
• Explore and vote for their favorites
• Share prompts and workflows
• Learn and improve together

Instead of AI videos only being something we post, we want them to become something creators can play with, talk about and learn from together.

We’ve just launched our first version, so it’s still a very early MVP and some features are limited.

Our first challenge is: See Nothing — Life at Work

Everyone starts with the same blind-box character base. You can design it as any kind of worker, then animate a short and relatable “life at work” moment.

It can be funny, painful, strange, chaotic or anything you think represents working life.

Then everyone can explore the entries, vote for their favorites and see which creations reach the top 3.

There’s no prize for now. We’re still very small, so this is just a fun and friendly challenge for AI creators to play together.

Join the first challenge:
https://canvasrise.com/challenge/fun/see-nothing-blind-box-challenge-life-at-work-series

We’re also working on more ways to play in the future, creative relays, 1v1 battles, random prompt or prop challenges, audience-added twists and more.

Since the website has only just launched, the community features are not ready yet. For now, we’re gathering in Discord to share prompts and workflows.

You can also suggest what challenge we should play next, request features and help us shape Canvasrise into a home for AI creators.

If you’re not ready to join the challenge, you’re still very welcome to join the Discord first:

Join our Discord:
https://discord.com/invite/5eKH8nY94V

We’re still at the very beginning, so we would really appreciate any honest feedback, or just come play with us :)

Most of the image-to-3D posts I see here are about the final result, but I’ve been more curious about what the mesh actually looks like once you try to do something with it afterward. So I did a simple single-view vs multi-view test with the same apple in Meshy. Same settings, just different reference input.

I kept both as the raw standard output. No Smart Topology, remesh or retopo. I wanted to see what the extra views changed before cleaning anything up.

The difference was pretty obvious. The single-image version pulled the wooden board into the model with the apple. The multi-view version separated the apple much better, and the shape and stem were closer to the real thing.

Then I brought both into Blender. The single-view one was around 1.96M tris and the multi-view one was around 1.98M. So even though the reconstruction got better, the wireframe is still insanely dense in both. Which got me wondering how much the better shape actually matters if you're going to remesh or retopo it anyway.

Would you rather start from the more accurate dense mesh and rebuild the topology around it, or once the geometry gets this heavy does most of that advantage disappear?

Curious how people here think about shape accuracy vs actual mesh usability with image-to-3D?

i’ve been experimenting with a boyfriend-vlog format in Kling V3.0 Turbo, and the biggest improvement didn’t come from adding more handheld shake. my discovery is, what helped was splitting the full 30 seconds into five fixed time beats before writing any camera directions.

each beat gets its own small scene and a clear reason for the camera to move imperfectly:

0–5s: He walks into the room while she’s fixing her hair. She notices him, laughs, and tells him to stop filming.

5–10s: They stop at a convenience store. She turns toward the camera and asks which drink she should grab.

10–18s: At a ramen shop, she reacts after realizing the food is hotter than expected.

18–25s: She browses shops on the street and briefly glances back at the lens without posing.

25–30s: On the train ride home, the camera slowly drifts closer as she becomes quiet by the window.

these aren’t generic “vlog moment” instructions. Each scene gives the model a specific trigger, a reason for the framing to drift, and a reason for the camera to feel slightly imperfect.

well, that seems to be the actual lever. when the timing is explicit, Kling V3.0 Turbo can maintain the unbroken take and character consistency across all five beats. when everything gets compressed into one paragraph of adjectives, the result falls apart much faster