🔥 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

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

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

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

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

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

A new approach to AI video generation is here.... MiniMax H3 combines multimodal understanding with generation, allowing it to work with text, images, video, and audio as part of the same creative process. Creators can use multiple references for characters, motion and camera movement, while also controlling edits and voices. Cinematic output reaches up to 2K at 24FPS with native stereo audio. Explore it on MiniMax Design (H3): #MiniMaxH3show more

Seedance 2.0 is now LIVE on the Pollo AI... App (iOS & Android) I’ve actually tried Seedance 2.0 on Pollo AI, and I can say it’s a solid upgrade. The motion feels more natural, and having audio and visuals generated together makes the whole output more cohesive. What stood out for me most is the level of control — being able to adjust performance, lighting, and camera really changes how you approach storytelling. 🔥 Plus, grab 60% OFF for a limited time on Pollo AI! Perfect for anyone diving into AI video creation.show more

MiniMax H3 is now on Magnific and honestly, there’s... a lot you can do with it. You can mix text, images, videos and audio in a single prompt up to 9 images, 3 videos and 3 audio references. Start creating now: It can generate up to 15s of 2K video with synced sound, including voice, music and effects. And you can go beyond generation too: edit clips, remove objects, transfer motion, and control the camera, character and voice. But the multi-reference workflow is probably my favorite. Give it your product, character, environment and motion references, and H3 pulls everything together. It feels like a much easier way to go from an idea to an actual finished video.show more

Depth Any Video with Scalable Synthetic Data AI physicists... and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.show more

Super excited about Hydra-0 from Hongyu Li and team!... The key idea is to use flow as a shared visual interface across embodiments/objects for controllable video generation, allowing a single generalist world model to learn from human, handheld-gripper, and robot interaction data. My favorite result is the video below: start from a real video of a human doing the task (left), extract the desired object flow, and condition the model on that flow (right). The model then hallucinates a plausible robot motion that could produce the same object motion. Very cool glimpse of how a generalist world model can bridge human demonstrations and robot control.show more

Kling AI 3.0 is here And it’s a serious... step forward for AI video. This update isn’t about small tweaks. It’s about refinement, realism, and making AI video feel ready for real-world use. What stands out with Kling 3.0? Stronger visual quality. Movements feel smoother. Lighting looks more natural. Scenes feel intentional instead of generated. Better prompt understanding. You can describe complex scenes, moods, or camera directions, and Kling 3.0 interprets them with much more accuracy. Less trial and error. More usable results. Improved motion and consistency. Characters stay consistent. Objects behave logically. Shots flow more naturally from start to finish. Greater creative control. From product-style visuals to cinematic storytelling, Kling 3.0 gives creators the flexibility to move beyond simple clips and into structured, high-quality sequences. The difference is subtle until you see it, and then it’s obvious. AI video is evolving quickly, and Kling AI 3.0 shows how far the technology has come. It’s not just about generating video anymore. It’s about generating video that’s usable, polished, and ready for campaigns, storytelling, and branded content. If you’re building with AI, this is one of the tools worth paying attention to, best AI video model for pro & commercial productionshow more

Seedance 2.0 is insane... AI filmmaking is no longer... locked behind complex workflows, expensive tools, or regional limits. SJinn Agent now supports both Seedance 2.0 Pro and Seedance 2.0 Fast, giving creators a faster way to generate cinematic videos with more control over the final result. You can add image, video, and audio references to guide the direction, motion, style, and feeling of your videos, making the process more like directing And the best part: they’re offering 40% off, so this is probably the easiest time to test what high-level AI video creation can actually look like Prompt in first comment:show more

1/ We've all been aware of the hype surrounding... Dreamina Seedance 2.0, and I finally got early access to the tool. And wow, I'm blown away. This is by far the best. It is starting to make AI video feel less like "generate a clip" and more like "direct a scene." What stood out to me is the level of control: camera motion, pacing, visual consistency, and the ability to build from multiple references inside one workflow. Some of the prompt directions that feel especially strong: - a busy modern city square during daytime. Suddenly, time freezes completely - a single continuous camera movement through a natural landscape that transitions through all four seasons in one shot - an underwater bioluminescent city waking up at dawn The big shift is this: One Prompt, Viral Remade. Edit Videos as Easy as Editing Photos. Dreamina Seedance 2.0 feels like a real step toward AI-native directing rather than just AI generation. Here are some examples 🧵:show more

There’s a weird paradox in AI video right now:... The more ambitious the shot, the less you want the model improvising everything. For a simple clip, “describe → generate” can be enough. But for a complex scene, it helps to define the world first. That’s the idea behind the GPT-6 Astra workflow I’ve been testing: build the 3D scene, establish spatial relationships, and define the camera and character paths in the Viewport. Then, manually bring everything into CapCut PC. Seedance 2.5 handles the video rendering, while CapCut PC takes the result through post-production. Spatial control before generation. Creative control after it. #CapCutPC #GPT6Astra #AIVideo #Seedance25show more

One of the most underrated ideas in AI filmmaking... might be previsualization. Traditional film crews don’t wait until the final shoot to figure out where the camera should move or how actors should travel through a space. You plan those things first. A similar idea is starting to make sense for AI video. GPT-6 Astra can understand and map the 3D environment, while the Viewport provides a way to establish camera movement and character paths before rendering. Then manually bring the scene into CapCut PC and let Seedance 2.5 turn that structure into video. From there, CapCut PC can take the footage through the editing process. Maybe 3D isn’t just another way to generate AI video. Maybe it becomes the pre-production layer AI filmmaking has been missing. CapCut PC is an interesting place to experiment with that workflow. #CapCutPC #GPT6Astra #AIVideo #Seedance25show more