{"author":{"@type":"Person","name":"Alexander Bock","url":"https://abxk.ai/en/autor/alexander-bock/"},"categories":["AI Productivity"],"content":{"markdown":"Type one sentence, and shortly afterward you have a video clip of a barista in a café at sunrise. Then you generate the second shot: the barista has a different face, and the cup has turned into a glass. A finished video needs more than good prompts. It needs a plan, shots that fit together, an edit with sound and a final check.\n\nThis is part 3 of Level 3 in our AI Productivity learning path. Here you take a short clip from script to release: script and shot list, one prompt per shot, consistent characters, edit and sound. You will also learn when an AI avatar is the better choice, where generated video fails and what to label. The part builds on [your first AI image](/en/ai-productivity/learn/create-your-first-ai-image/) from Level 1.\n\n## Plan the video before you generate anything\n\nA **text-to-video** tool turns a written description into a short video clip. Many tools also accept an image as the starting point; this is called **image-to-video**. One generation usually gives you one **shot**: a continuous piece of video between two cuts, often only a few seconds long. Some tools can extend a clip, but quality often suffers the longer it runs. So a 30-second video is a sequence of short shots, like a film.\n\nThat is why AI video works best as a small production in three phases. You plan the whole video, generate it shot by shot and finish it in an editor.\n\n![Three phases from script to clip: plan with script and shot list, generate keyframes and shots, finish with edit, sound, checks and label; weak shots go back to generation](/images/learn/en/text-to-video-from-script-to-clip-pipeline.webp \"The production pipeline: plan first, generate shot by shot, finish in the editor. Weak shots go back for a new attempt.\")\n\nGive planning and finishing more time than generating: each generation takes a while and usually uses up credits or a quota. Decide on the format first, too: 16:9 for websites and presentations, 9:16 for vertical videos on phones. If you crop later, you lose part of the picture.\n\nPlanning also saves energy. Generating video is energy-intensive, and The Guardian names text-to-video prompts among the uses that are easy to skip when they serve no real purpose (Source: The Guardian, March 2026). A clear shot list means fewer generations that end up in the trash.\n\n### Write the script\n\nStart with the message, not the pictures. Who watches the video, where, and what should viewers do afterward? Then write the script in two columns: **what we see** and **what we hear**. This classic format keeps picture and sound in step. Read the spoken part aloud with a stopwatch, because it sets the length of the video.\n\n### Turn the script into a shot list\n\nA **shot list** breaks the script into numbered shots. Each row fixes what we see, what we hear, the length and how you will make the shot. Show one action per shot. \"She pours the milk\" works. \"She pours the milk, serves the cup and waves\" is three shots.\n\nNot every shot has to be generated. Real product photos, a screen recording or a filmed handshake are often better and more honest. Generate what would be expensive or impossible to film: a mood, a place, an idea. In this invented example, a café announces new opening hours in a 20-second social clip:\n\n| # | Shot | What we see | What we hear | Length | Made with |\n|---|---|---|---|---|---|\n| 1 | Wide shot, slow push-in | The café from the street at sunrise; the lights go on | Street sounds, music starts | 4 s | Text-to-video |\n| 2 | Close-up | The barista pours milk into a cup | Voice: \"Mornings start earlier now.\" | 4 s | Image-to-video from a keyframe |\n| 3 | Medium shot | She hands the cup across the counter and smiles | Voice: \"From Monday, we open at 6:30.\" | 4 s | Image-to-video from a keyframe |\n| 4 | Close-up | Steam rises from the cup on the counter | Music | 3 s | Image-to-video from a keyframe |\n| 5 | End card | Logo and opening hours as text | Voice: \"See you tomorrow.\" | 5 s | Editor, no AI |\n\nShot 5 follows a simple rule: text goes into the edit, not into a prompt. Video models can garble letters, and a typo in your opening hours is a real problem.\n\n## Write one prompt per shot\n\nA good shot prompt reads like an instruction to a camera crew, from the camera move to the sound.\n\n![A prompt for shot 2 of the café clip, split into seven labeled parts: shot and camera, subject, action, setting and light, style, format and length, sound; four parts stay the same in every shot](/images/learn/en/text-to-video-from-script-to-clip-shot-prompt.webp \"Anatomy of a shot prompt. Subject, setting, style and format stay word for word the same in every shot; only shot, action and sound change.\")\n\nThree habits make the difference:\n\n- **Describe the motion.** A prompt that only describes a picture gets random movement. Say what moves, where and how fast: \"the camera slowly pushes in\", \"steam rises\".\n- **Keep the fixed parts identical.** Copy the character, the setting and the style word for word into every prompt of the video.\n- **Make variants, change one thing.** Generate two or three variants per shot and keep the best. If none works, change one part of the prompt, just as with images. Save the prompt and settings of every shot you keep.\n\nVideo models understand the vocabulary of film. These words are a good start:\n\n| Words | What the viewer sees | Use it for |\n|---|---|---|\n| Wide shot | The whole scene, people are small | The first shot of a scene: where are we? |\n| Medium shot | A person from the waist up | Actions and gestures |\n| Close-up | A face, a hand or an object fills the frame | Details and emotion |\n| Static camera | The camera does not move | Calm shots that are easy to cut |\n| Pan, tilt | The camera turns sideways or up and down | Showing a room or a tall object |\n| Push-in, pull-out | The camera moves toward or away from the subject | Drawing attention, revealing the scene |\n| Tracking shot | The camera follows a moving subject | Walking, driving, moving through a place |\n\nOur comparison lists {{\u003c stat \"area\" \"text-to-video\" \u003e}} [text-to-video tools](/en/ai-productivity/#area-text-to-video), and our [ABXK Ranking of text-to-video generators](/en/ai-productivity/best-text-to-video-generators/) shows which of them lead right now. These four are common starting points for generated scenes:\n\n{{\u003c tools \"google-veo runway kling luma\" \u003e}}\n\n## Keep characters and style consistent\n\nEvery generation starts from scratch. Two prompts that describe \"a barista in a green apron\" give you two different people, often in different light. Viewers notice at once.\n\nThe most reliable fix is to separate the look from the motion. First, you create still images of the key moments in an image generator. These **keyframes** are quicker to make and to fix than video, and you approve them before anything moves. Then you animate each keyframe with image-to-video. The prompt now only describes the motion.\n\n![Comparison: the same text prompt generated three times gives three different-looking people; the same keyframe animated three times keeps the same person, only the motion changes](/images/learn/en/text-to-video-from-script-to-clip-consistency.webp \"Text-to-video lets the model decide the look anew each time. Image-to-video keeps the look you approved and adds motion.\")\n\nFour more techniques help:\n\n1. **A character sheet:** one clear image of each character, from the front and from the side, as the reference for every keyframe.\n2. **Reference images and frames:** many tools take pictures of a character, an object or a place as a guide. According to Google, Veo works with reference images and can animate from a first frame to a last frame that you provide. Luma is known for this kind of image-to-video, too.\n3. **One style block:** describe light, colors and look once, and paste the text unchanged into every prompt.\n4. **One model per video:** each model has its own look, so shots from mixed models do not match.\n\nThen check by eye. Put all shots side by side on the timeline and compare faces, clothes and colors.\n\n## Edit the shots and add sound\n\nGenerated shots are raw material; the video is made in the editor. Place the shots in order and trim each one to its best seconds, because a generated clip is rarely good from start to end. Cut on movement, for example while the barista hands over the cup. Match brightness and color across shots. Add titles, names and numbers in the edit, where you can correct them.\n\nBuild the sound in three layers:\n\n- **Voice:** the spoken part of the script, recorded by you or made with an AI voice.\n- **Music:** a track whose license covers your use.\n- **Atmosphere and effects:** street noise, the hiss of the coffee machine. Small sounds make generated pictures feel real.\n\nSome models, such as Google Veo, create sound together with the picture, according to the vendor. Check that sound like the picture: does it fit, and do the lips match the words? Keep the voice clearly above the music. Part 4 of this level covers [AI voice and music](/en/ai-productivity/learn/ai-voice-and-music-for-content/) in depth. Captions and final checks are in the Level 2 part on how to [edit audio and video with AI](/en/ai-productivity/learn/edit-audio-and-video-with-ai/).\n\n## Avatar video or generative video?\n\nText-to-video tools come in two kinds. **Generative video** tools such as Google Veo, Runway, Kling AI and Luma create scenes, places and camera moves. In **avatar video**, a digital presenter, the **avatar**, speaks your script with matching lip movements. You pick a ready-made avatar or create one of a real person, with that person's consent. The script is then almost the whole production.\n\n| | Avatar video | Generative video |\n|---|---|---|\n| What you put in | A script, an avatar and a voice | One prompt per shot, often with a reference image |\n| What you get | A presenter who speaks to the camera | Scenes, places, products and moods in motion |\n| Typical uses | Training, how-to videos, internal updates, product explainers | Ads, social clips, B-roll, concept films |\n| Consistency | The same presenter in every video, by design | Planned shot by shot with keyframes and references |\n| Changes | Edit the script and render again | Generate the shot again, and it may look different |\n| Other languages | Some tools translate the speech and match the lips | You add a new voice track in the edit |\n| Main risks | Stiff delivery; a real person's likeness needs consent | Physics errors, changing faces, garbled text |\n| Examples | Synthesia, HeyGen | Google Veo, Runway, Kling AI, Luma |\n\nAccording to the vendors, Synthesia creates avatar videos mainly for training and communication, and HeyGen translates videos with lip-sync into many languages.\n\n{{\u003c tools \"synthesia heygen\" \u003e}}\n\nIf someone needs to explain something that changes often or needs several languages, start with an avatar. To show a place, a mood or a product in motion, use generative video. Or combine them: the avatar explains, and generated **B-roll**, extra shots that illustrate what is said, shows it.\n\n## Where generated video still fails\n\nVideo models improve quickly, but some errors keep coming back, and many are easy to miss on a phone. Watch every shot at least once at full size and in slow motion.\n\n![Six typical weak spots of generated video: length, physics, hands and faces, text and logos, continuity, speech and lips — each with a way around it](/images/learn/en/text-to-video-from-script-to-clip-limits.webp \"Six weak spots of generated video and how to plan around them.\")\n\nPlan around these limits:\n\n- Keep shots short and simple: one subject, one action, a calm camera.\n- Cut away before a movement goes wrong. Three good seconds beat six seconds with a glitch.\n- Avoid close-ups of hands doing complex things, such as typing. Show the result instead.\n- Animate real products from real photos, and compare every frame with the original. Models can change shapes, labels and buttons.\n\nIf a shot fails three times, rewrite it. A different angle or a simpler action usually beats more attempts.\n\n## Labels and rights before you publish\n\n**Labels.** YouTube, for example, asks creators to disclose content that viewers could easily mistake for a real person, place, scene or event. Clearly unrealistic content, such as animation, needs no label, and neither does AI help with a script (Source: YouTube, \"How we're helping creators disclose altered or synthetic content\", 2024, blog.youtube). In the EU, the AI Act requires professional users who create a **deepfake** to disclose it. A deepfake is AI-made image, audio or video content that resembles real people, objects, places or events and would falsely appear authentic. For evidently artistic, satirical or fictional works, a disclosure that does not spoil the work is enough (Source: EU AI Act, Articles 3 and 50). Many generators also mark their output: Google says that videos made with Veo carry its SynthID watermark. Never remove such marks.\n\n**Rights.** Four questions cover most cases:\n\n- **Do the tool's terms allow your use?** Commercial use depends on the tool and often on the plan.\n- **Did every real person agree?** Create an avatar or a voice of a real person only with written consent, for an agreed purpose.\n- **Does the music license cover your channel?**\n- **Who owns the result?** In many countries, including the US and Germany, copyright protects only works created by people. The US Copyright Office concluded in 2025 that prompts alone do not make you the author of generated output. Your own work can still be protected: the script, the selection and arrangement of shots, and your edits (Source: US Copyright Office, Copyright and Artificial Intelligence, Part 2: Copyrightability, 2025, copyright.gov).\n\n{{\u003c callout type=\"warning\" title=\"No realistic fakes of real people\" \u003e}}\nNever generate a realistic video of a real person without their written consent. Even as a joke, such a clip can damage a reputation and violate someone's rights. Our [AI Security](/en/ai-security/) section explains how to spot deepfakes.\n{{\u003c /callout \u003e}}\n\nUse this checklist before every release:\n\n| Check | Question to ask |\n|---|---|\n| Message | Does the clip say what the script says, and nothing more? |\n| Consistency | Do faces, clothes, colors and light match across all shots? |\n| Errors | Did you watch every shot slowly, at full size, for hands, physics and text? |\n| Text | Are all titles, names, dates and numbers set in the edit and correct? |\n| Sound | Is the voice clear, the music licensed and the mix even? |\n| Captions | Are captions on, correct and in sync? |\n| Label | Is the AI label set where the platform or the law requires it? |\n| Rights | Do the tool's terms allow your use, and is every consent on file? |\n\n{{\u003c callout type=\"try\" \u003e}}\nMake a 15- to 20-second clip for a real occasion, such as a team announcement or an event teaser. Write the script in two columns and break it into four or five shots. Create keyframes, animate them, and add voice, music and captions. Then note which shot needed the most attempts, and why. It shows you what to plan better next time.\n{{\u003c /callout \u003e}}","text":"Type one sentence, and shortly afterward you have a video clip of a barista in a café at sunrise. Then you generate the second shot: the barista has a different face, and the cup has turned into a glass. A finished video needs more than good prompts. It needs a plan, shots that fit together, an edit with sound and a final check.\nThis is part 3 of Level 3 in our AI Productivity learning path. Here you take a short clip from script to release: script and shot list, one prompt per shot, consistent characters, edit and sound. You will also learn when an AI avatar is the better choice, where generated video fails and what to label. The part builds on your first AI image from Level 1.\nPlan the video before you generate anything A text-to-video tool turns a written description into a short video clip. Many tools also accept an image as the starting point; this is called image-to-video. One generation usually gives you one shot: a continuous piece of video between two cuts, often only a few seconds long. Some tools can extend a clip, but quality often suffers the longer it runs. So a 30-second video is a sequence of short shots, like a film.\nThat is why AI video works best as a small production in three phases. You plan the whole video, generate it shot by shot and finish it in an editor.\nThe production pipeline: plan first, generate shot by shot, finish in the editor. Weak shots go back for a new attempt. Give planning and finishing more time than generating: each generation takes a while and usually uses up credits or a quota. Decide on the format first, too: 16:9 for websites and presentations, 9:16 for vertical videos on phones. If you crop later, you lose part of the picture.\nPlanning also saves energy. Generating video is energy-intensive, and The Guardian names text-to-video prompts among the uses that are easy to skip when they serve no real purpose (Source: The Guardian, March 2026). A clear shot list means fewer generations that end up in the trash.\nWrite the script Start with the message, not the pictures. Who watches the video, where, and what should viewers do afterward? Then write the script in two columns: what we see and what we hear. This classic format keeps picture and sound in step. Read the spoken part aloud with a stopwatch, because it sets the length of the video.\nTurn the script into a shot list A shot list breaks the script into numbered shots. Each row fixes what we see, what we hear, the length and how you will make the shot. Show one action per shot. \u0026ldquo;She pours the milk\u0026rdquo; works. \u0026ldquo;She pours the milk, serves the cup and waves\u0026rdquo; is three shots.\nNot every shot has to be generated. Real product photos, a screen recording or a filmed handshake are often better and more honest. Generate what would be expensive or impossible to film: a mood, a place, an idea. In this invented example, a café announces new opening hours in a 20-second social clip:\nText-to-Video: From Script to Finished Clip # Shot What we see What we hear Length Made with 1 Wide shot, slow push-in The café from the street at sunrise; the lights go on Street sounds, music starts 4 s Text-to-video 2 Close-up The barista pours milk into a cup Voice: \u0026ldquo;Mornings start earlier now.\u0026rdquo; 4 s Image-to-video from a keyframe 3 Medium shot She hands the cup across the counter and smiles Voice: \u0026ldquo;From Monday, we open at 6:30.\u0026rdquo; 4 s Image-to-video from a keyframe 4 Close-up Steam rises from the cup on the counter Music 3 s Image-to-video from a keyframe 5 End card Logo and opening hours as text Voice: \u0026ldquo;See you tomorrow.\u0026rdquo; 5 s Editor, no AI Shot 5 follows a simple rule: text goes into the edit, not into a prompt. Video models can garble letters, and a typo in your opening hours is a real problem.\nWrite one prompt per shot A good shot prompt reads like an instruction to a camera crew, from the camera move to the sound.\nAnatomy of a shot prompt. Subject, setting, style and format stay word for word the same in every shot; only shot, action and sound change. Three habits make the difference:\nDescribe the motion. A prompt that only describes a picture gets random movement. Say what moves, where and how fast: \u0026ldquo;the camera slowly pushes in\u0026rdquo;, \u0026ldquo;steam rises\u0026rdquo;. Keep the fixed parts identical. Copy the character, the setting and the style word for word into every prompt of the video. Make variants, change one thing. Generate two or three variants per shot and keep the best. If none works, change one part of the prompt, just as with images. Save the prompt and settings of every shot you keep. Video models understand the vocabulary of film. These words are a good start:\nText-to-Video: From Script to Finished Clip (2) Words What the viewer sees Use it for Wide shot The whole scene, people are small The first shot of a scene: where are we? Medium shot A person from the waist up Actions and gestures Close-up A face, a hand or an object fills the frame Details and emotion Static camera The camera does not move Calm shots that are easy to cut Pan, tilt The camera turns sideways or up and down Showing a room or a tall object Push-in, pull-out The camera moves toward or away from the subject Drawing attention, revealing the scene Tracking shot The camera follows a moving subject Walking, driving, moving through a place Our comparison lists 11 text-to-video tools, and our ABXK Ranking of text-to-video generators shows which of them lead right now. These four are common starting points for generated scenes:\nGoogle VeoText-to-Video · AI at the coreGoogle\u0026#39;s video model; generates clips with sound from text or images, in Gemini and Flow. RunwayText-to-Video · AI at the coreVideo generator and editor with detailed control over camera and motion. Kling AIText-to-Video · AI at the coreVideo generator from Kuaishou; known for realistic human motion and longer clips. LumaText-to-Video · AI at the coreGenerates video and images with its own AI models and creative agents. Short descriptions from our tool comparison, based on the vendors' information — not tested by ABXK.\nKeep characters and style consistent Every generation starts from scratch. Two prompts that describe \u0026ldquo;a barista in a green apron\u0026rdquo; give you two different people, often in different light. Viewers notice at once.\nThe most reliable fix is to separate the look from the motion. First, you create still images of the key moments in an image generator. These keyframes are quicker to make and to fix than video, and you approve them before anything moves. Then you animate each keyframe with image-to-video. The prompt now only describes the motion.\nText-to-video lets the model decide the look anew each time. Image-to-video keeps the look you approved and adds motion. Four more techniques help:\nA character sheet: one clear image of each character, from the front and from the side, as the reference for every keyframe. Reference images and frames: many tools take pictures of a character, an object or a place as a guide. According to Google, Veo works with reference images and can animate from a first frame to a last frame that you provide. Luma is known for this kind of image-to-video, too. One style block: describe light, colors and look once, and paste the text unchanged into every prompt. One model per video: each model has its own look, so shots from mixed models do not match. Then check by eye. Put all shots side by side on the timeline and compare faces, clothes and colors.\nEdit the shots and add sound Generated shots are raw material; the video is made in the editor. Place the shots in order and trim each one to its best seconds, because a generated clip is rarely good from start to end. Cut on movement, for example while the barista hands over the cup. Match brightness and color across shots. Add titles, names and numbers in the edit, where you can correct them.\nBuild the sound in three layers:\nVoice: the spoken part of the script, recorded by you or made with an AI voice. Music: a track whose license covers your use. Atmosphere and effects: street noise, the hiss of the coffee machine. Small sounds make generated pictures feel real. Some models, such as Google Veo, create sound together with the picture, according to the vendor. Check that sound like the picture: does it fit, and do the lips match the words? Keep the voice clearly above the music. Part 4 of this level covers AI voice and music in depth. Captions and final checks are in the Level 2 part on how to edit audio and video with AI.\nAvatar video or generative video? Text-to-video tools come in two kinds. Generative video tools such as Google Veo, Runway, Kling AI and Luma create scenes, places and camera moves. In avatar video, a digital presenter, the avatar, speaks your script with matching lip movements. You pick a ready-made avatar or create one of a real person, with that person\u0026rsquo;s consent. The script is then almost the whole production.\nText-to-Video: From Script to Finished Clip (3) Avatar video Generative video What you put in A script, an avatar and a voice One prompt per shot, often with a reference image What you get A presenter who speaks to the camera Scenes, places, products and moods in motion Typical uses Training, how-to videos, internal updates, product explainers Ads, social clips, B-roll, concept films Consistency The same presenter in every video, by design Planned shot by shot with keyframes and references Changes Edit the script and render again Generate the shot again, and it may look different Other languages Some tools translate the speech and match the lips You add a new voice track in the edit Main risks Stiff delivery; a real person\u0026rsquo;s likeness needs consent Physics errors, changing faces, garbled text Examples Synthesia, HeyGen Google Veo, Runway, Kling AI, Luma According to the vendors, Synthesia creates avatar videos mainly for training and communication, and HeyGen translates videos with lip-sync into many languages.\nSynthesiaText-to-Video · AI at the coreCreates videos with AI avatars from a script, mainly for training and communication. HeyGenText-to-Video · AI at the coreAvatar video platform with lip-synced translation into many languages. Short descriptions from our tool comparison, based on the vendors' information — not tested by ABXK.\nIf someone needs to explain something that changes often or needs several languages, start with an avatar. To show a place, a mood or a product in motion, use generative video. Or combine them: the avatar explains, and generated B-roll, extra shots that illustrate what is said, shows it.\nWhere generated video still fails Video models improve quickly, but some errors keep coming back, and many are easy to miss on a phone. Watch every shot at least once at full size and in slow motion.\nSix weak spots of generated video and how to plan around them. Plan around these limits:\nKeep shots short and simple: one subject, one action, a calm camera. Cut away before a movement goes wrong. Three good seconds beat six seconds with a glitch. Avoid close-ups of hands doing complex things, such as typing. Show the result instead. Animate real products from real photos, and compare every frame with the original. Models can change shapes, labels and buttons. If a shot fails three times, rewrite it. A different angle or a simpler action usually beats more attempts.\nLabels and rights before you publish Labels. YouTube, for example, asks creators to disclose content that viewers could easily mistake for a real person, place, scene or event. Clearly unrealistic content, such as animation, needs no label, and neither does AI help with a script (Source: YouTube, \u0026ldquo;How we\u0026rsquo;re helping creators disclose altered or synthetic content\u0026rdquo;, 2024, blog.youtube). In the EU, the AI Act requires professional users who create a deepfake to disclose it. A deepfake is AI-made image, audio or video content that resembles real people, objects, places or events and would falsely appear authentic. For evidently artistic, satirical or fictional works, a disclosure that does not spoil the work is enough (Source: EU AI Act, Articles 3 and 50). Many generators also mark their output: Google says that videos made with Veo carry its SynthID watermark. Never remove such marks.\nRights. Four questions cover most cases:\nDo the tool\u0026rsquo;s terms allow your use? Commercial use depends on the tool and often on the plan. Did every real person agree? Create an avatar or a voice of a real person only with written consent, for an agreed purpose. Does the music license cover your channel? Who owns the result? In many countries, including the US and Germany, copyright protects only works created by people. The US Copyright Office concluded in 2025 that prompts alone do not make you the author of generated output. Your own work can still be protected: the script, the selection and arrangement of shots, and your edits (Source: US Copyright Office, Copyright and Artificial Intelligence, Part 2: Copyrightability, 2025, copyright.gov). !No realistic fakes of real people\nNever generate a realistic video of a real person without their written consent. Even as a joke, such a clip can damage a reputation and violate someone\u0026rsquo;s rights. Our AI Security section explains how to spot deepfakes.\nUse this checklist before every release:\nText-to-Video: From Script to Finished Clip (4) Check Question to ask Message Does the clip say what the script says, and nothing more? Consistency Do faces, clothes, colors and light match across all shots? Errors Did you watch every shot slowly, at full size, for hands, physics and text? Text Are all titles, names, dates and numbers set in the edit and correct? Sound Is the voice clear, the music licensed and the mix even? Captions Are captions on, correct and in sync? Label Is the AI label set where the platform or the law requires it? Rights Do the tool\u0026rsquo;s terms allow your use, and is every consent on file? ▸Try it\nMake a 15- to 20-second clip for a real occasion, such as a team announcement or an event teaser. Write the script in two columns and break it into four or five shots. Create keyframes, animate them, and add voice, music and captions. Then note which shot needed the most attempts, and why. It shows you what to plan better next time.\n","words":2447},"date":"2026-10-14","description":"Plan a short AI video shot by shot: script, shot list, one prompt per shot, consistent characters, editing and sound — plus avatars, limits, labels and rights.","image":"https://abxk.ai/images/cover/text-to-video-from-script-to-clip.webp","json":"https://abxk.ai/en/ai-productivity/learn/text-to-video-from-script-to-clip/index.json","kind":"page","language":"en","learning_path":{"category":"ai-productivity","level":3,"level_name":"Advanced"},"modified":"2026-10-14","related_tools":["Google Veo","Runway","Kling AI","Luma","Synthesia","HeyGen"],"section":"ai-productivity","tags":["text-to-video","ai-video","video-production","image-to-video"],"title":"Text-to-Video: From Script to Finished Clip","translations":[{"json":"https://abxk.ai/de/ai-productivity/learn/text-to-video-from-script-to-clip/index.json","language":"de","url":"https://abxk.ai/de/ai-productivity/learn/text-to-video-from-script-to-clip/"}],"url":"https://abxk.ai/en/ai-productivity/learn/text-to-video-from-script-to-clip/"}