🔥 AI video creation is finally evolving beyond basic... motion prompts. Try out Depth Map Control right here: Historically, the hardest part of the process has been maintaining scene consistency: • camera movement • object placement • spatial relationships Thanks to PixVerse Depth Map Control, you can now grab depth data from a reference video and merge it with a fresh image to explore highly directed video generation. The final outcome? Much smoother camera tracking. Highly cohesive visual scenes. It is a powerful workflow that hands creators actual authority over how their AI visuals shift and flow over time. 🎬show more

Guys, Depth Map Control now live on Mini Apps... The workflow is quite easy: reference video -> depth map video -> reference image + depthvideo -> a video that follows motion and space more precisely. RT + Follow + Reply = 150 Credits in DMs (72H ONLY)show more

I handed a dance video over to AI... Try... this exact workflow yourself: I used PixVerse Depth Map Control to seamlessly map the original choreography onto a completely fresh character. The visual outcome is incredibly fascinating 👀 Instead of generating a scene from absolute zero, this specific setup helps you lock down crucial details from your base clip: • movement • body position • spatial structure Your source footage dictates the precise action, while a single reference picture establishes the new aesthetic. It is a brilliant way to level up your AI video experiments — granting you far more precision over how your animations move and shift.show more

MiniMax H3 is a serious upgrade for AI video... creation. Instead of relying on just text prompts, you can combine images, videos, and audio references to control the character, motion, camera, and sound. What stands out: → Up to 9 image + 3 video + 3 audio references → Native synced voice, music & sound effects → 2K video generation up to 15 seconds → Instruction-based video editing → Motion transfer → Better character, camera & voice control The interesting part is that you can build a full scene from references instead of endlessly regenerating until the result feels right. 🔗 Now is a great time to try MiniMax H3 on Magnific, especially with the current 2K offer.show more

I finally stopped writing prompts like "cinematic tracking shot... with smooth camera movement." Because honestly, it never looked right. Dreamina Canvas just added Extract Motion — take a video reference, extract the motion and depth, and build something completely new from it. With a full month of Dreamina Basic for $1.5 — up to 90% off, till Oct 9. #Dreamina #DreaminaPartnershow more

AI video models are getting way more powerful. Seedance... 2.5 inside Magnific creates smoother motion, stronger camera control, and more consistent scenes. One prompt → directed scene → polished AI video. Here is the breakdown:show more

I think the hardest part of AI video isn’t... always getting a result. It’s getting a result you can actually keep developing. Motion capture in Dreamina Canvas gives creators another way to start - bring character motion and spatial depth from a reference video into a new scene - then build from there. A full month of Dreamina Basic is $1.50 - with credits for up to 9 ten-second Seedance videos or 393 GPT Image 2.5 images. Dreamina 2.0 is canvas-first - the new way AI video is made. #Dreamina #DreaminaPartnershow more

Depth video workflows have been getting a lot of... attention lately, so I tested one myself. Combined with Seedance 2.0, it produced more natural motion-transfer results than using Kling Motion Control directly. Why use a depth video? 1. It removes the original character and scene details, reducing copyright and sensitive-content risks. 2. It preserves the original motion, timing, and spatial structure. This separates motion extraction from visual generation, allowing you to recreate the movement with better models and any reference character. We’ve also launched a free online tool that converts regular videos into depth videos—no local setup required: In the example below, we converted a dance video from Douyin into a depth video, then regenerated it with a reference character using Seedance 2.0. The original choreography and timing are preserved, while the lighting adapts naturally to the new character and scene.show more

OpenArt just launched Seedance 2.0 for Teams and Enterprise... It delivers the kind of camera control, reference depth, and cinematic storytelling most AI video tools still struggle to offer You can use up to 9 image references, 3 videos, and 3 audio files to build multi-shot scenes with far more control than a normal prompt 10 prompts worth saving:show more

Kling 4.0 is pushing AI filmmaking to a whole... new level. With sharper visuals, smoother camera transitions, and lifelike expressions, every scene feels more realistic and cinematic. Improved motion accuracy and precise camera control bring greater depth and immersion to visual storytelling. Kling AI continues to unlock exciting new possibilities for AI-powered filmmaking.show more

I think people are still underestimating how important the... 3D layer could become in AI video. The problem with complex AI shots has never just been image quality. It’s spatial consistency, camera direction and knowing where everything is supposed to be. GPT-6 Astra can map the scene and keep camera + character paths within the Viewport, giving the shot a spatial structure before generation. I then manually move that scene into CapCut PC and use Seedance 2.5 to render it. From there, CapCut PC becomes the actual editing environment. That feels less like “AI generated a video” and more like a production pipeline worth experimenting with. #CapCutPC #GPT6Astra #AIVideo #Seedance25show more

Getting a character to move the way you imagined... is harder than it looks. Dreamina Canvas has motion capture, so you can bring character motion and spatial depth from a reference video into a new scene instead of trying to explain every movement in words. A full month of Dreamina Basic is just $1.50, with credits for up to 9 ten-second Seedance videos or 393 GPT Image 2.5 images. Dreamina 2.0 is canvas-first, the new way AI video is made. #Dreamina #DreaminaPartnershow more

THE DEPTH MAP TRICK THAT ALSO WORKS ON EXPRESSION,... NOT JUST DANCE — GPT IMAGE 2 × SEEDANCE 2.0 Feed the model raw footage of someone gesturing, reacting, moving through a scene, and it tries to interpret everything at once - the face, the outfit, the alley, the light, and somewhere in there, the actual motion. Feed it a depth map instead and there's nothing left to read but the movement itself. Most creators assume this trick only applies to dance, where the motion is clean and repetitive. It works just as well on loose, expressive, conversational movement - the kind with no fixed rhythm to lean on. The problem was never the model's ceiling. It's that raw footage hands it ten variables when the only one that matters is how the body is moving. Here's the workflow 1. Lock the character reference in GPT Image 2 first - face, build, outfit, so identity holds no matter how expressive or loose the motion gets 2. Convert the source footage into a depth map instead of feeding the raw video - this strips out the original performer's face, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference - two separate inputs, two separate jobs 4. Let the depth map carry only spatial movement - gesture, weight shift, head turn, with no competing information about who's moving or what they look like 5. Keep the two inputs isolated all the way through - the moment appearance data leaks into the motion reference, the model starts negotiating between two identities Why this works - Raw footage hands the model everything at once, and loose, expressive motion has nowhere near as much rhythm to lean on as dance does - A depth map is pure spatial information, so unscripted gesture reads just as cleanly as choreography - Separating identity from motion means the character stays locked while every twitch and turn still survives intact - The gain isn't a smarter model, it's a model with fewer decisions left to make Use cases: ⁃ Expression and gesture transfer, not just choreography ⁃ Conversational or reactive scenes with no fixed rhythm ⁃ Any footage where identity needs to change but the exact motion can't ⁃ Character showcase content built on found or reference footage The character sheet still answers who's moving. The depth map still answers how - it just turns out how applies to a lot more than dancing.show more

here's how you can create videos with full camera... control using Krea Agent: - take an input image and ask the agent to create a 3D scene with it. - after the scene is created, ask it to create any camera movement you want. - finally, ask it to render the 3D video with Seedance 2.5. try it now 👇show more

AI video is becoming less about generating a clip... and more about controlling the entire scene. 🎬 MiniMax H3 is now available on Magnific, with a multimodal workflow that brings text, images, video and audio together. → Up to 9 image + 3 video + 3 audio references → 2K video up to 15 seconds → Native synced voice, music & sound effects → Motion transfer → Instruction-based video editing → Camera, character and voice control The interesting part is how much you can build from references. A moodboard, product, character, environment or motion reference can all become part of the same generation. And the sound is generated alongside the video, instead of being added afterward. MiniMax H3 on Magnific makes the whole process feel much closer to directing a scene than simply prompting a video.show more

Here’s a revised Depth Map Storyboard workflow. This workflow... gives you much more control over the storyboard and produces more consistent results. Workflow below 🧵show more

The funniest part of AI video is being able... to describe an entire fantasy world but somehow failing to explain one camera move. Dreamina Canvas just added Extract Motion, so thankfully I can stop trying. Feed it a reference, pull out the motion and spatial depth, build from there. With a full month of Dreamina Basic for $1.5 — up to 90% off, till Oct 9. #Dreamina #DreaminaPartnershow more

Bruce Lee vs Chuck Norris, restaged with Mozart and... Bach. The new Video Depth Anything model in FilmSpark.ai turns any video into a depth map you can use as a motion reference with Seedance 2.0. Live now in the Video Edit UI.show more

Most AI video tools generate clips. The interesting ones... help you build a complete creative workflow. MiniMax H3 stood out because it can use text, images, audio, and video together with Omni Reference. That makes it easier to keep the style, branding, and creative direction consistent from the first prompt to the final video. What caught my attention: • Text + image + audio + video inputs • Native 2K video generation • Better control over edits and refinements • Built for ads, e-commerce, gaming, and branded content AI video is moving beyond single prompts. It is becoming a creative workspace.MiniMax Design (H3) Try here 👇 #hailuoai #hailuo #MiniMaxH3 #H3 #AIVideo #GenerativeAIshow more

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN... SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.show more

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN... SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.show more