正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

how to prompt undetectable ai shots while designing a running scene, first think about these 3 basic questions: how does the camera move? what is it looking at? where does it stop? a good motion prompt is really just a timeline it needs to follow real-world physics, and it... needs to carry story at the same time 1. start with the narrative goal of the shot camera movement is not just movement it is the storytelling so before writing the prompt, define what the shot is trying to do for example, in a 15-second one take, the goal could be: follow the female lead laterally while she runs, to build speed and tension then briefly reveal the people chasing her then let the camera hesitate for a moment and find her again that small “lose and recapture” moment adds spatial depth and makes the chase feel more intense 2. build a clear space for the camera to work in if the space is vague, the shot gets messy very fast i like breaking the scene into layers so the model knows where everything belongs foreground: passing objects, environmental motion main subject layer: the woman running midground: cafe tables, pedestrians, the people chasing her background: the vanishing point of the street, and the entrance to the pedestrian area once the space is clear, the camera has a stage to move through 3. describe motion like a physical process a good moving shot has to respect inertia if the movement feels weightless or too perfect, it instantly feels fake so instead of using broad words, describe a chain of actions the camera can actually perform what accelerates what slows down when it adjusts when it slightly overshoots when it catches itself again that little bit of imperfection is usually what makes it feel real 4. use focus as part of the storytelling in a one take, focus is one of the best ways to guide attention you can design moments where focus shifts with intention for example: focus briefly drifts from the chasers’ faces, passes beyond them, lands on the woman in the distance, then quickly pulls back again it feels like a small mistake, but that’s exactly why it works it simulates a camera operator re-evaluating the subject in the middle of a fast-moving shot that kind of temporary focus loss and recovery adds a lot of immediacy and documentary feeling 5. build the sound space with the camera sound should move with the shot when the camera turns toward the chasers, the woman’s breathing should fall deeper into the sound field, while the chasers’ footsteps and breathing move to the center when the camera finds the woman again, her breath becomes the main sound again that shift in audio perspective helps the scene feel much more immersive it’s not just about what we see it’s also about where we feel the scene from 6. use negative constraints to stop common ai mistakes this part matters a lot i usually add clear “don’ts” at the end to stop the model from breaking the shot for example: no cuts no teleporting zooms no sliding characters no body fusion no floating props the travel bag must keep believable weight and inertia these negative constraints act like guardrails they help keep the result inside a believable physical world for me, the core of a strong motion prompt is simple: organize space, camera, focus, action, and sound into one executable timeline that’s really the difference instead of prompting a vague feeling, you’re designing a physical process and that’s usually what helps ai generate a moving shot that feels coherent, grounded, and full of tensionshow more

el.cine

139,587 subscribers

14,644 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

This library turns “I know how the shot should feel, but I can’t describe it” into a visual reference and a ready-to-use camera movement prompt in seconds Pick the movement Copy the prompt Stop wasting generations trying to explain the camera It looks like a small tool But small tools like this often become the pages people save, share and quietly return to whenever the same problem appears again That is how useful content starts becoming an asset instead of another post lost in the feed I explored the wider system behind that idea in the article below Save this for your next shot

This library turns “I know how the shot should feel, but I can’t describe it” into a visual reference and a ready-to-use camera movement prompt in seconds Pick the movement Copy the prompt Stop wasting generations trying to explain the camera It looks like a small tool But small tools like this often become the pages people save, share and quietly return to whenever the same problem appears again That is how useful content starts becoming an asset instead of another post lost in the feed I explored the wider system behind that idea in the article below Save this for your next shot

Cip⚡️

12,905 次观看 • 2 个月前

🚨 AI VIDEO PROMPTING IS DEAD !! You no longer need to write a 500-word prompt just to tell AI how your video should look. 💀 I just tested Pippit’s 3D Director Studio + Seedance 2.5 And instead of explaining the entire scene with words… I literally BUILT the scene first. For my test, I created a cinematic fight scene with characters inside a 3D environment. I could control: 1️⃣ Where the characters stood 2️⃣ Which props they held 3️⃣ Where the camera was placed 4️⃣ What each character should do Then I could actually CHOREOGRAPH the action. → Run → Fight → Shoot → Fall → React → Move You can place these actions on the timeline, adjust when they happen, and even plan the camera movement and character trajectories. So instead of: PROMPT → GENERATE → PRAY 😂 It becomes: BUILD → DIRECT → GENERATE And that gives you WAY more control. Because you’re not asking AI to imagine the entire scene from a block of text. You’re literally showing it the: → Characters → Environment → Props → Positions → Actions → Camera Then Seedance 2.5 takes that 3D direction and turns it into the final video. That’s what makes this so interesting. It feels less like prompting an AI video model… And more like sitting in the DIRECTOR’S CHAIR. 🎬 Try Pippit: Pippitofficial #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

🚨 AI VIDEO PROMPTING IS DEAD !! You no longer need to write a 500-word prompt just to tell AI how your video should look. 💀 I just tested Pippit’s 3D Director Studio + Seedance 2.5 And instead of explaining the entire scene with words… I literally BUILT the scene first. For my test, I created a cinematic fight scene with characters inside a 3D environment. I could control: 1️⃣ Where the characters stood 2️⃣ Which props they held 3️⃣ Where the camera was placed 4️⃣ What each character should do Then I could actually CHOREOGRAPH the action. → Run → Fight → Shoot → Fall → React → Move You can place these actions on the timeline, adjust when they happen, and even plan the camera movement and character trajectories. So instead of: PROMPT → GENERATE → PRAY 😂 It becomes: BUILD → DIRECT → GENERATE And that gives you WAY more control. Because you’re not asking AI to imagine the entire scene from a block of text. You’re literally showing it the: → Characters → Environment → Props → Positions → Actions → Camera Then Seedance 2.5 takes that 3D direction and turns it into the final video. That’s what makes this so interesting. It feels less like prompting an AI video model… And more like sitting in the DIRECTOR’S CHAIR. 🎬 Try Pippit: Pippitofficial #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

SANI BULA

95,462 次观看 • 25 天前

What if you could choreograph an entire action scene before generating a single frame? 🥋🎬 That’s what I wanted to test with Pippitofficial’s 3D Director Studio + Seedance 2.5. I created a scene with two girls demonstrating a series of karate moves and used the 3D workspace to figure out the sequence first. The interesting part is that I wasn’t relying on a giant prompt to explain every movement. I could place both characters in the scene, set up the environment, choose props from 150+ available options, and then map out how the performance should unfold. One move at a time. Step in → stance → punch → block → kick → reset → next move. The timeline makes it possible to arrange actions and control their timing, while movement trajectories help define where the characters should go. And then there’s the camera. Instead of hoping the generated video finds the right angle, I could plan the camera movement around the choreography and decide how the sequence should be captured. That’s the part I found most useful. You can think through the scene visually before asking AI to generate the final result. Characters. Movement. Timing. Camera. All planned first. Then Seedance 2.5 turns that setup into the actual video. For me, this feels like a shift from simply prompting AI to actually directing the shot. Try Pippit 3D Director Studio: #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

What if you could choreograph an entire action scene before generating a single frame? 🥋🎬 That’s what I wanted to test with Pippitofficial’s 3D Director Studio + Seedance 2.5. I created a scene with two girls demonstrating a series of karate moves and used the 3D workspace to figure out the sequence first. The interesting part is that I wasn’t relying on a giant prompt to explain every movement. I could place both characters in the scene, set up the environment, choose props from 150+ available options, and then map out how the performance should unfold. One move at a time. Step in → stance → punch → block → kick → reset → next move. The timeline makes it possible to arrange actions and control their timing, while movement trajectories help define where the characters should go. And then there’s the camera. Instead of hoping the generated video finds the right angle, I could plan the camera movement around the choreography and decide how the sequence should be captured. That’s the part I found most useful. You can think through the scene visually before asking AI to generate the final result. Characters. Movement. Timing. Camera. All planned first. Then Seedance 2.5 turns that setup into the actual video. For me, this feels like a shift from simply prompting AI to actually directing the shot. Try Pippit 3D Director Studio: #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

Shabnam Parveen

51,749 次观看 • 26 天前

AI video is getting very good at generating impressive clips. But the harder question is: can you actually control what happens inside the shot? That’s why I find the 3D-first approach interesting. GPT-6 Astra maps the space and helps establish camera + character movement within the Viewport before rendering. Then I manually bring the scene into CapCut PC for Seedance 2.5 rendering. And the part I didn’t expect to care about as much? The render is still not the final answer. I can keep editing and shaping it in CapCut PC. Maybe AI video is moving from “generate a clip” to “build a shot.” #CapCutPC #GPT6Astra #AIVideo #Seedance25

AI video is getting very good at generating impressive clips. But the harder question is: can you actually control what happens inside the shot? That’s why I find the 3D-first approach interesting. GPT-6 Astra maps the space and helps establish camera + character movement within the Viewport before rendering. Then I manually bring the scene into CapCut PC for Seedance 2.5 rendering. And the part I didn’t expect to care about as much? The render is still not the final answer. I can keep editing and shaping it in CapCut PC. Maybe AI video is moving from “generate a clip” to “build a shot.” #CapCutPC #GPT6Astra #AIVideo #Seedance25

Katyayani Shukla

84,107 次观看 • 27 天前

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.

Rina

45,651 次观看 • 2 个月前

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 #Seedance25

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 #Seedance25

Manish Kumar Shah

30,785 次观看 • 27 天前

Synchronizing music with movement in Seedance 2 Omni: Prompt + tip: Only Omni offers you that option. You just need to add a 13-second music clip to avoid errors, and specify in the prompt that it should follow the music's cadence; here is the prompt I used: It shows the Beautiful mystical woman (image_1) moving to the rhythm of this music: (audio_1) in a fluid and powerful way. with fiery ethereal serpent of fire. Dynamic shot circling the character. no music. asmr sound tip: The generator tends to alter the music slightly. The best option is to set it not to include the music and then add it during editing. Omni is also useful for adding dialogue.

Synchronizing music with movement in Seedance 2 Omni: Prompt + tip: Only Omni offers you that option. You just need to add a 13-second music clip to avoid errors, and specify in the prompt that it should follow the music's cadence; here is the prompt I used: It shows the Beautiful mystical woman (image_1) moving to the rhythm of this music: (audio_1) in a fluid and powerful way. with fiery ethereal serpent of fire. Dynamic shot circling the character. no music. asmr sound tip: The generator tends to alter the music slightly. The best option is to set it not to include the music and then add it during editing. Omni is also useful for adding dialogue.

hedoήist

33,890 次观看 • 2 个月前

Blender just became a prompt box. Kimi K3 + Blender MCP can take a simple text prompt and build an entire 3D scene for you. terrain, buildings, lighting, materials, camera movement, animation, even the Python scripts holding everything together. but the crazy part isn’t just that it can build the scene. Kimi can inspect what it created, understand what looks wrong, then go back into Blender and fix the actual scene before rendering again. the camera can be clipping through a tree, the lighting can feel off, the city can look too artificial, and instead of starting over, Kimi can make the changes directly inside Blender. and because everything is happening in Blender, you’re not left with a locked AI-generated video. you get the actual 3D scene. every object, material, light, camera and keyframe stays editable. a few years ago, creating something like this could take weeks of modeling, scripting, lighting and animation. now you can describe the world, let Kimi build the first 90%, and spend your time making the final 10% actually look good. that’s what makes Kimi K3 + Blender MCP so interesting. it’s getting dangerously close to text-to-3D, except the result is a real Blender project you can keep editing.

Blender just became a prompt box. Kimi K3 + Blender MCP can take a simple text prompt and build an entire 3D scene for you. terrain, buildings, lighting, materials, camera movement, animation, even the Python scripts holding everything together. but the crazy part isn’t just that it can build the scene. Kimi can inspect what it created, understand what looks wrong, then go back into Blender and fix the actual scene before rendering again. the camera can be clipping through a tree, the lighting can feel off, the city can look too artificial, and instead of starting over, Kimi can make the changes directly inside Blender. and because everything is happening in Blender, you’re not left with a locked AI-generated video. you get the actual 3D scene. every object, material, light, camera and keyframe stays editable. a few years ago, creating something like this could take weeks of modeling, scripting, lighting and animation. now you can describe the world, let Kimi build the first 90%, and spend your time making the final 10% actually look good. that’s what makes Kimi K3 + Blender MCP so interesting. it’s getting dangerously close to text-to-3D, except the result is a real Blender project you can keep editing.

MIKE

10,434 次观看 • 1 个月前

We may be overestimating the importance of the generation step in AI video. Getting a model to produce a beautiful 10-second clip is impressive. But filmmaking has never been just about producing footage. It’s about deciding what happens before the camera rolls, controlling the shot, and shaping the material afterward. That’s why this workflow is interesting: GPT-6 Astra establishes the 3D space and camera + character movement in the Viewport. The scene is manually brought into CapCut PC. Seedance 2.5 renders the video. Then CapCut PC takes the result through post-production. Generation becomes one stage in a larger pipeline rather than the finish line. Maybe that’s the direction AI video needs to take if it’s going to move beyond “cool clips” and toward actual filmmaking workflows. And CapCut PC is a pretty natural place to experiment with that full process. #CapCutPC #GPT6Astra #AIVideo #Seedance25

We may be overestimating the importance of the generation step in AI video. Getting a model to produce a beautiful 10-second clip is impressive. But filmmaking has never been just about producing footage. It’s about deciding what happens before the camera rolls, controlling the shot, and shaping the material afterward. That’s why this workflow is interesting: GPT-6 Astra establishes the 3D space and camera + character movement in the Viewport. The scene is manually brought into CapCut PC. Seedance 2.5 renders the video. Then CapCut PC takes the result through post-production. Generation becomes one stage in a larger pipeline rather than the finish line. Maybe that’s the direction AI video needs to take if it’s going to move beyond “cool clips” and toward actual filmmaking workflows. And CapCut PC is a pretty natural place to experiment with that full process. #CapCutPC #GPT6Astra #AIVideo #Seedance25

AIMATRIX

16,778 次观看 • 27 天前

Only one Adobe Firefly video prompt from our research hit production-ready on the first generation. Prompt in comments 👀 It looks you have to direct the camera as much as the model to get the most out of Firefly. Every other video session in our research needed a revision. Revising the first frame is where Firefly broke: >motion failures (temporal flicker, motion continuity, physics in motion) carried 60% of video's issue mix once designers re-prompted. TLDR: in Adobe Firefly, the camera does a lot of the work. Bake it into the first prompt and the video lands. Its by far the best time to be a creative.

Only one Adobe Firefly video prompt from our research hit production-ready on the first generation. Prompt in comments 👀 It looks you have to direct the camera as much as the model to get the most out of Firefly. Every other video session in our research needed a revision. Revising the first frame is where Firefly broke: >motion failures (temporal flicker, motion continuity, physics in motion) carried 60% of video's issue mix once designers re-prompted. TLDR: in Adobe Firefly, the camera does a lot of the work. Bake it into the first prompt and the video lands. Its by far the best time to be a creative.

ben @ CVPR

12,847 次观看 • 4 个月前

THE REAL TEST IS WHAT HAPPENS WHEN SHE DISAPPEARS At first, it looks like a normal POV clip from a crowded carnival booth: plush bears above, people moving through the frame, and a girl paying with her phone. But then she gets fully blocked twice. First by a man carrying a child, then by another group passing through the same shot. Both times, she disappears from view, and both times she returns exactly the same: same spot, same posture, same payment motion, same phone, same hand, same angle. That is where AI video usually breaks. It is easy to keep a subject stable when nothing covers them. The harder part is restoring them after a full obstruction without changing the face, hands, outfit, pose, timing or objects. Here, the scene holds. No face shift, no hand glitch, no outfit drift, no phone jump, no reset after the frame clears. And the wild part is that none of it was filmed: not the booth, not the plush toys, not the strangers crossing the camera. AI video is starting to survive the messy interruptions that make real footage feel real

THE REAL TEST IS WHAT HAPPENS WHEN SHE DISAPPEARS At first, it looks like a normal POV clip from a crowded carnival booth: plush bears above, people moving through the frame, and a girl paying with her phone. But then she gets fully blocked twice. First by a man carrying a child, then by another group passing through the same shot. Both times, she disappears from view, and both times she returns exactly the same: same spot, same posture, same payment motion, same phone, same hand, same angle. That is where AI video usually breaks. It is easy to keep a subject stable when nothing covers them. The harder part is restoring them after a full obstruction without changing the face, hands, outfit, pose, timing or objects. Here, the scene holds. No face shift, no hand glitch, no outfit drift, no phone jump, no reset after the frame clears. And the wild part is that none of it was filmed: not the booth, not the plush toys, not the strangers crossing the camera. AI video is starting to survive the messy interruptions that make real footage feel real

Inspector

25,265 次观看 • 17 天前

SHE GETS COVERED BY STRANGERS TWICE AND STILL COMES BACK UNCHANGED This looks like a regular POV clip from a busy carnival booth. Stuffed bears are hanging above, people are moving through the crowd, and she is standing at the counter paying with her phone. Then the real stress test happens. A man holding a kid walks directly in front of the camera and completely hides her. A few seconds later, another woman with a child and another man pass through the same frame and block her again. Both times, she vanishes from the shot. And both times, she returns exactly the same: same position, same stance, still mid-payment, phone in the same hand at the same angle. This is where AI video usually starts to fall apart. Keeping a subject stable when nothing blocks them is easy. The difficult part is when someone crosses the lens and the model has to bring back what was hidden without changing the face, hands, clothes, pose, timing or objects. Here, nothing obvious breaks. No face shift. No hand glitch. No outfit change. No phone jumping. No weird reset after the camera gets blocked. And the craziest part is that none of this was real. Not the carnival booth. Not the stuffed bears. Not the strangers crossing the shot at exactly the right time. That is why this clip works so well. AI is no longer just making clean pretty frames. It is starting to keep continuity through chaotic, real-world movement.

SHE GETS COVERED BY STRANGERS TWICE AND STILL COMES BACK UNCHANGED This looks like a regular POV clip from a busy carnival booth. Stuffed bears are hanging above, people are moving through the crowd, and she is standing at the counter paying with her phone. Then the real stress test happens. A man holding a kid walks directly in front of the camera and completely hides her. A few seconds later, another woman with a child and another man pass through the same frame and block her again. Both times, she vanishes from the shot. And both times, she returns exactly the same: same position, same stance, still mid-payment, phone in the same hand at the same angle. This is where AI video usually starts to fall apart. Keeping a subject stable when nothing blocks them is easy. The difficult part is when someone crosses the lens and the model has to bring back what was hidden without changing the face, hands, clothes, pose, timing or objects. Here, nothing obvious breaks. No face shift. No hand glitch. No outfit change. No phone jumping. No weird reset after the camera gets blocked. And the craziest part is that none of this was real. Not the carnival booth. Not the stuffed bears. Not the strangers crossing the shot at exactly the right time. That is why this clip works so well. AI is no longer just making clean pretty frames. It is starting to keep continuity through chaotic, real-world movement.

Performance

2,486,682 次观看 • 21 天前

I've seen a lot of animatics lately that are super detailed, essentially viewport previews of the final shot. But an animatic at it's core doesn't need to be anything fancy. Its main purpose is to plan and test the timing, camera angles, movement, and overall composition. With animatics you have to keep in mind the following: Pre-visualization: It allows to see a basic version of the animation or scene before committing to detailed work. Timing and pacing: An animatic helps identify how long each scene or shot should last, ensuring that the timing feels right. Planning: It helps with layout, camera angles, and transitions. By visualizing the shots, the team can ensure that the framing and overall design of the scenes work well in 3D space. Efficiency: It allows the team to test and fix any potential issues early on, like awkward movements or awkward pacing, before spending time on high-quality rendering or complex animation. In short, an animatic helps in conceptualizing the final animation by giving a low-res, rough version of the scenes, which guides the entire production process in terms of visual design, timing, and storytelling.

I've seen a lot of animatics lately that are super detailed, essentially viewport previews of the final shot. But an animatic at it's core doesn't need to be anything fancy. Its main purpose is to plan and test the timing, camera angles, movement, and overall composition. With animatics you have to keep in mind the following: Pre-visualization: It allows to see a basic version of the animation or scene before committing to detailed work. Timing and pacing: An animatic helps identify how long each scene or shot should last, ensuring that the timing feels right. Planning: It helps with layout, camera angles, and transitions. By visualizing the shots, the team can ensure that the framing and overall design of the scenes work well in 3D space. Efficiency: It allows the team to test and fix any potential issues early on, like awkward movements or awkward pacing, before spending time on high-quality rendering or complex animation. In short, an animatic helps in conceptualizing the final animation by giving a low-res, rough version of the scenes, which guides the entire production process in terms of visual design, timing, and storytelling.

Voxyde

22,244 次观看 • 1 年前

This feels less like ""AI image generation"" and more like actually directing a shot. Turns out GPT-6 Astra + Dreamina Seedance 2.5 is an actual pipeline. I started with a real photo and used GPT-6 Astra + Blender to recreate it as a clay-style scene. Then I used Seedance 2. 5 to figure out the movement and camera motion. The last step happens in Dreamina - turning that clay animation into a live-action result. The separation of structure → motion → final look is what makes this workflow interesting. #Dreamina #DreaminaPartner

This feels less like ""AI image generation"" and more like actually directing a shot. Turns out GPT-6 Astra + Dreamina Seedance 2.5 is an actual pipeline. I started with a real photo and used GPT-6 Astra + Blender to recreate it as a clay-style scene. Then I used Seedance 2. 5 to figure out the movement and camera motion. The last step happens in Dreamina - turning that clay animation into a live-action result. The separation of structure → motion → final look is what makes this workflow interesting. #Dreamina #DreaminaPartner

Enzo Sanchez | IA

56,485 次观看 • 23 天前

Seedance 2.0 has a fix for character drift almost nobody uses. Every new text description is a fresh interpretation of appearance. That's where the drift comes from: a slightly different face, a slightly different outfit, proportions that shift from scene to scene. The @ Image tag solves this directly — attach a photo once, and it becomes the fixed source of truth for face, outfit, and proportions across the entire multi-shot sequence. The @ Video tag works the same way for motion and camera — tag a reference instead of describing it, and the model copies that exact style instead of an approximation. You're not making the model remember. You're just stopping yourself from reminding it differently every single time.

Seedance 2.0 has a fix for character drift almost nobody uses. Every new text description is a fresh interpretation of appearance. That's where the drift comes from: a slightly different face, a slightly different outfit, proportions that shift from scene to scene. The @ Image tag solves this directly — attach a photo once, and it becomes the fixed source of truth for face, outfit, and proportions across the entire multi-shot sequence. The @ Video tag works the same way for motion and camera — tag a reference instead of describing it, and the model copies that exact style instead of an approximation. You're not making the model remember. You're just stopping yourself from reminding it differently every single time.

Zentrix⌚️

88,931 次观看 • 2 个月前

I wanted this typography to feel like part of the story, not just text added over a video. I used Pexo to explore different ways the words could move with the idea, giving it clear direction around the timing, movement and transitions. From there, I refined the result with feedback and Mark to Fix until the animation felt right. That’s what I like about the workflow — I can focus on the creative idea and communicate what needs to change without getting lost in a traditional editing timeline. Pexo The tool behind this one:

I wanted this typography to feel like part of the story, not just text added over a video. I used Pexo to explore different ways the words could move with the idea, giving it clear direction around the timing, movement and transitions. From there, I refined the result with feedback and Mark to Fix until the animation felt right. That’s what I like about the workflow — I can focus on the creative idea and communicate what needs to change without getting lost in a traditional editing timeline. Pexo The tool behind this one:

Anissa

14,763 次观看 • 13 天前

A GIRL WALKED OFF THE TOP OF A WATERSLIDE, THREW TWO FLIPS DOWN THE FACE OF IT, AND LANDED IN THE POOL 20,000 likes None of it is generated. That is exactly why it is worth eleven seconds, because your feed has spent a year training you to assume the opposite run it as a test. here is what is in the frame that no model reliably puts there yet: → the water. spray leaves her body at the right angle and volume for her speed, and the sheet on the slide deforms under her weight instead of flowing past her → the rotation conserves. she tucks and the spin accelerates, she opens and it slows. angular momentum is a constraint, and generators approximate it rather than obey it → the crowd reacts late. people on the platform turn after she has already gone, because they are reacting, not choreographed → the camera operator loses her. the frame lags the subject and catches up. that is a mistake, and mistakes are the expensive thing to fake → and the landing is ugly. real impacts are. generated ones almost always resolve too cleanly none of that is a checklist you can memorise, and pretending otherwise is how people get caught. every one of those tells has a shelf life. three of them were already unreliable a year ago what does not expire is where you look. continuity, physics, and mistakes - the three places a model has to simulate a system instead of reproducing a surface. surfaces are solved. systems are not, yet and the reason this gets more useful every month is not fakes. it is that real footage is starting to get accused. the cost of being wrong now runs in both directions the fastest way to calibrate your own eye is to make one yourself. image-to-video from a still, one line about the motion - Picsart runs it from a phone. you start spotting the tells about ten minutes after you have made your own this one is real. the fact that you had to check is the actual story

A GIRL WALKED OFF THE TOP OF A WATERSLIDE, THREW TWO FLIPS DOWN THE FACE OF IT, AND LANDED IN THE POOL 20,000 likes None of it is generated. That is exactly why it is worth eleven seconds, because your feed has spent a year training you to assume the opposite run it as a test. here is what is in the frame that no model reliably puts there yet: → the water. spray leaves her body at the right angle and volume for her speed, and the sheet on the slide deforms under her weight instead of flowing past her → the rotation conserves. she tucks and the spin accelerates, she opens and it slows. angular momentum is a constraint, and generators approximate it rather than obey it → the crowd reacts late. people on the platform turn after she has already gone, because they are reacting, not choreographed → the camera operator loses her. the frame lags the subject and catches up. that is a mistake, and mistakes are the expensive thing to fake → and the landing is ugly. real impacts are. generated ones almost always resolve too cleanly none of that is a checklist you can memorise, and pretending otherwise is how people get caught. every one of those tells has a shelf life. three of them were already unreliable a year ago what does not expire is where you look. continuity, physics, and mistakes - the three places a model has to simulate a system instead of reproducing a surface. surfaces are solved. systems are not, yet and the reason this gets more useful every month is not fakes. it is that real footage is starting to get accused. the cost of being wrong now runs in both directions the fastest way to calibrate your own eye is to make one yourself. image-to-video from a still, one line about the motion - Picsart runs it from a phone. you start spotting the tells about ten minutes after you have made your own this one is real. the fact that you had to check is the actual story

Valentin

600,828 次观看 • 11 天前

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis

Erika 

235,152 次观看 • 6 个月前

Is 3D becoming the missing middle layer between AI imagination and controllable video? Text can describe what a scene should look like. Video models can render it. But something still has to define the spatial relationships in between. That’s where GPT-6 Astra becomes interesting: it can understand the 3D environment and establish camera and character movement inside the Viewport. The resulting scene is manually brought into CapCut PC, where Seedance 2.5 handles the video generation. Then the generated footage can continue into CapCut PC for editing. It’s not really “3D replacing AI video.” It’s more like giving AI video a spatial blueprint to follow. That feels like a direction worth experimenting with in CapCut PC. #CapCutPC #GPT6Astra #AIVideo #Seedance25

Is 3D becoming the missing middle layer between AI imagination and controllable video? Text can describe what a scene should look like. Video models can render it. But something still has to define the spatial relationships in between. That’s where GPT-6 Astra becomes interesting: it can understand the 3D environment and establish camera and character movement inside the Viewport. The resulting scene is manually brought into CapCut PC, where Seedance 2.5 handles the video generation. Then the generated footage can continue into CapCut PC for editing. It’s not really “3D replacing AI video.” It’s more like giving AI video a spatial blueprint to follow. That feels like a direction worth experimenting with in CapCut PC. #CapCutPC #GPT6Astra #AIVideo #Seedance25

Onil Coder

12,000 次观看 • 27 天前