🛒 The Sora AI in-depth application solution for video creators and film and television teams covers core scenarios such as Wensheng video, Tusheng video, video editing, scene extension, and rapid visualization of storyboards, leveraging Sora's understanding of the physical world and the advantages of movie-level image generation.

Sora AI video generation depth solution

Solution overview

This solution is aimed at video creators, film and television planners, short video operators and advertising creative personnel in the media, film and television industry, and provides an end-to-end workflow for AI video generation with Sora as the core. The solution covers core creative scenarios such as Vincent Video, Tusheng Video, video extended editing, storyboard visualization, Cameo image implantation, etc., giving full play to Sora's ability to understand the physical world and its advantages in movie-level image generation.

Since its research preview debut in February 2024, Sora has redefined the boundaries of AI video generation capabilities - from continuous 60-second long video generation to audio and video synchronization output, from physical world simulation to cross-camera character consistency, each capability is rewriting the answer to "what kind of video can a machine generate?" Although Sora has officially been discontinued in April 2026, the workflow paradigm it pioneered—text ideation → draft generation → editing iteration → social distribution—has become the standard reference model for AI video creation. This plan not only summarizes the best practices during the active period of Sora, but also provides creators who rely on Sora with a migration path to alternative tools such as Runway, Kling, and Pika.

Target users: short video creators, film and television pre-planners, advertising creatives, social media operators, independent producers.

Program expected income:

• The first draft generation cycle of a single concept video is reduced from 2-3 days to 15-30 minutes
• Storyboard visualization is converted from hand-drawing/outsourcing to AI instant generation, reducing iteration costs by 80%
• The mass production of video materials has been changed from weekly scheduling to daily scheduling.

Toolchain list

Tools	Purpose	Required Account Level	Estimated Fees	Alternatives
Sora	Wensheng Video/Tusheng Video/Video Extension (deactivated)	ChatGPT Plus/Pro	$20-$200/month	Runway/Kling/Pika
ChatGPT	Creative planning and prompt word engineering	Free version/Plus version $20/month	On-demand billing	Claude/Gemini
Runway	Alternative video generation and refinement editing	Free/Pro version	$15-$95/month	Kling/Pika
Kling	Medium-length video generation alternative	Free version/paid version	Pay-as-you-go	Runway/Pika
Pika	Video generation and stylized editing	Free version/Pro version	$10-$50/month	Runway/Kling
CapCut	Post-production mixing and editing and special effects overlay	Free version/Pro version $7.99/month	Pay-as-you-go billing	Premiere Pro

Preparation

Account and environment preparation

• [ ] Confirm that the Sora data export portal (sora.chatgpt.com) can still be accessed to export historical generated data
• [ ] Register and open an alternative tool account (at least one for Runway/Kling/Pika)
• [ ] Register a ChatGPT account for prompt word planning and creative divergence
• [ ] Install video post-production tools (CapCut or equivalent software)

Material preparation

• [ ] Organize reference video materials and mood boards
• [ ] Prepare product images/scenario images for Tusheng video testing
• [ ] Prepare Cameo digital clone to record material (if you still have access to Sora App to export)

Cognitive Preparation

• [ ] Understand the randomness and screening cost of Sora output quality - a single video needs an average of 3-5 times to pass the quality screening
• [ ] Clarify the positioning of AI video: early inspiration/POC tools vs. final delivery materials

Step-by-step guide

Step 1: Creative planning and prompt word engineering

⏱ Estimated time: 1-2 hours 🎯 Goal: Convert creative concepts into actionable video descriptions and prompts ⚠️ Prerequisites: ChatGPT account ready

Operation instructions

Use AI dialogue tools to assist in creative divergence and prompt word polishing. The quality of Sora's output is highly dependent on the quality of the input description - the more specific the description of the scene, light, and composition, the higher the success rate of generation. This link directly determines the upper limit of the output quality of all subsequent steps.

Specific operations

1. Describe the video creative concept in ChatGPT and ask it to output a structured video storyboard description
2. According to Sora’s prompt word best practices, split the description into: scene environment + subject action + light atmosphere + camera movement + duration
3. Generate 3-5 variant prompt words for each storyboard for subsequent batch testing
4. For storyboard scenes, generate prompt words shot by shot and mark character/scene consistency constraints between shots.

Prompt Word Template (Deduction):

[Scene environment description], [Subject description] is [Action description]. [Light atmosphere], [Tone style].
Lens: [Motion Mode], [Scenery]. [Aspect Ratio].
Duration: Approximately [X] seconds.

Example:

On a rainy night at Shibuya Crossing in Tokyo, neon lights are reflected in the standing water. A woman in a red windbreaker walked across the zebra crossing holding a transparent umbrella, and rainwater fell along the edge of the umbrella. Cinematic tones, contrasting cool blues and warm oranges. Shot: Slowly advance from mid-range to close-up, keeping the focus on the character’s face. 16:9 aspect ratio. Duration: Approximately 15 seconds.

Verification method

• [ ] Each storyboard prompt contains at least 5 key descriptive dimensions (environment/subject/action/light/shot)
• [ ] Keep character/scene descriptions consistent across shots within the same storyboard
• [ ] The executable actions in the prompt word do not exceed Sora's ability boundaries (no abstract metaphors, no surreal physics)

Step 2: Generate the first draft of Vincent’s video

⏱ Estimated time: 1-3 hours (including screening time) 🎯 Goal: Generate a first draft of the video from the text prompt words and filter the available materials ⚠️ Prerequisites: The prompt word project is completed; the available video generation tool has been confirmed (such as Sora has been disabled, use Runway/Kling/Pika)

Operation instructions

Input the prompt words polished in step 1 into the video generation tool to perform batch generation and quality screening. This is the most time-consuming and patient link in the entire workflow - the probability of "satisfactory once generated" for AI videos is usually in the 20-30% range, so an efficient screening and elimination mechanism needs to be established.

Specific operations

1. Input the 3-5 variant prompt words of each story into the generation tool in sequence
2. Generate at least 2-3 candidate videos for each prompt word
3. Establish screening criteria: physical consistency (objects do not cross the mold, normal gravity), picture quality (no obvious artifacts/flickers), style matching (close to the expected visual style)
4. Archive the passed candidate videos into three-level categories: "directly available/requires editing/unqualified"
5. For storyboards with a failure rate of more than 80%, return to step 1 to polish the prompt words again.

Practical Suggestions for Screening:

• Key points of physical consistency inspection: whether the characters' limbs are abnormally twisted, whether the background objects are stable, and whether the shadow directions are consistent
• Key points for picture quality inspection: whether there is flicker noise, whether the edges of the subject are clear, and whether there are any sudden changes in color.
• Physical compliance rate reference: Sora 2 officially claims 88%, but in real continuous generation (non-selected samples), the compliance rate is about 60-70%

Verification method

• [ ] Each storyboard has at least 1 candidate video at the "ready to use" or "requires editing" level
• [ ] The physical abnormal points of all selected videos have been marked (for reference for later repair)
• [ ] The unqualified prompt word has recorded the failure mode and returned to the iteration

Step 3: Tusheng video and visual reference generation

⏱ Estimated time: 1-2 hours 🎯 Goal: Turn existing concept maps, mood boards, or product images into dynamic video clips ⚠️ Prerequisite: Already have reference image material

Operation instructions

The core value of Tusheng Video is to "give AI a visual anchor" - compared with plain text descriptions, input images can significantly reduce the deviation between the final output and creative expectations. It is suitable for product demonstrations, dynamic previews of concept designs, and complex scenes where plain text generation is not effective.

Specific operations

1. Prepare reference images: product pictures with white background have the best effect, while complex artistic illustrations have unstable effects.
2. Upload the image to the video generation tool, appending prompt words describing the dynamic content
3. Focus on: whether the subject is deformed, whether the background dynamics are natural, and whether the original details in the image are retained.
4. For e-commerce products: One product image can generate 5-10 dynamic display videos from different angles
5. For film and television concept design: the mood board can be used as a unified visual anchor for the scene to ensure that the colors and styles of subsequent shots are consistent.

Selection decision of Tusheng Video vs. Wensheng Video:

• When there are physical objects/conceptual drawings, give priority to the video route - the success rate is about 30-40% higher than pure text generation
• When there is no reference image, first use ChatGPT/DALL-E to generate a concept map, and then generate a video - this is currently the path from "pure concept to dynamic video" with the highest success rate
• For scenes that require precise control of the appearance of the subject (such as brand logo animation), you must take the Tusheng video route

Verification method

• [ ] The appearance of the subject in the generated video is consistent with the input image (no unexpected distortion)
• [ ] The dynamic effect meets expectations (the movement speed, direction, and amplitude are reasonable)
• [ ] The generated video can be used as the basic material for subsequent editing steps.

Step 4: Video expansion, editing and mixing

⏱ Estimated time: 2-4 hours 🎯 Goal: Expand, connect and mix existing video materials to form a complete narrative segment ⚠️ Prerequisites: At least 2 video clips that pass the preliminary screening

Operation instructions

Video expansion and mixing are Sora’s key capabilities that differentiate it from pure text generation tools—it allows creators to iterate on existing material rather than starting from scratch. Sora's "forward expansion" (generating prequel content) has a higher success rate than "backward expansion" (continuing subsequent plots), because the former only needs to continue the existing visual style, while the latter needs to predict action logic that has not yet occurred. If Sora is no longer available, Runway's Gen-3/Gen-4 offer similar extended functionality.

Specific operations

1. Select a video that passes the preliminary screening as seed material
2. Perform forward expansion: enter a description of "what happened before this video" and generate 5-10 seconds of preamble content
3. Perform backward expansion: enter a description of "what happened after this video" and continue the subsequent content for 5-10 seconds
4. Use the video mixing function to make a transition between two videos with similar styles.
5. Check the character/scenario consistency of the expansion part - the consistency will drop significantly after more than 3 expansions. It is recommended to control it within 2-3 expansions.

Secure plan when lens connection fails:

• If the expansion/mixing results of the two shots cannot be visually connected, abandon the AI automatic transition and use traditional editing tools (CapCut/Premiere) to make hard cuts or transitions.
• In Runway, Gen-4's "Layer Editing" can achieve more precise local modifications, suitable for scenes that require refinement.
• In Kling, longer video generation duration (up to 2 minutes) reduces the need for frequency of scaling operations

Verification method

• [ ] The expanded video is consistent in visual style (color, lighting, depth of field) with the seed material
• [ ] The blending transition is natural, without obvious visual jump or character mutation.
• [ ] The total length of the video meets the expected narrative needs

Step 5: Cameo image placement and personalized appearance (exclusive for Sora 2)

⏱ Estimated time: 1-2 hours 🎯 Goal: Integrate the creator/actor's "digital clone" into the generated video scene ⚠️ Prerequisites: Cameo digital clone material recorded when Sora App is available (disabled, this step is a historical workflow reference)

Operation instructions

Cameo is the core differentiating capability of Sora 2 - users can pre-record short videos to create a "digital avatar", and then authorize their own image to be implanted into any generated scene in the Sora App. This feature upgrades AI videos from "pure visual experiments" to "creative forms that can recognize personal brands."

Specific operations

1. Use Sora App to record your personal digital avatar (simple clothing, clean background, and even facial lighting)
2. When generating a video, select the "Cameo" function and select the image to be implanted from the authorized library
3. Adjust the placement and proportion of the implant - ensure the digital clone blends into the lighting and perspective of the scene
4. After generation, check whether the facial features are stable and whether the lip shape and dialogue are synchronized.
5. In Remix mode, allow others to use your Cameo image for secondary creation within the scope of your authorization.

Alternative path after deactivation:

• HeyGen's digital human video solution can be used as an alternative to Cameo, supporting uploading photos to generate digital avatars and output videos
• Runway Gen-4's layer editing can achieve similar effects, but with longer operating paths
• The combined solution of traditional green screen keying + AI background generation is superior to pure AI implantation in terms of controllability

Verification method

• [ ] The facial features of the digital clone in the scene are clearly identifiable
• [ ] Lip sync is within acceptable range (within an error of less than 0.3 seconds)
• [ ] The lighting of the digital clone is consistent with the lighting of the scene

Step 6: Post-production mixing and multi-channel distribution

⏱ Estimated time: 1-2 days 🎯 Goal: Integrate all AI-generated clips into a complete video, add sound effects, subtitles, branding elements ⚠️ Preconditions: All video clips have been generated and filtered

Operation instructions

AI video generation solves the problem of "from 0 to 0.8" - the last 0.2 requires traditional post-production tools to complete. This step integrates the generated clips, AI soundtrack, subtitles and other materials into a finished video that can be directly distributed.

Specific operations

1. Import all filtered video clips in CapCut
2. Arrange in storyboard order and add transitions where styles break.
3. Use AI soundtrack tools (such as Suno/Udio) or material library soundtrack to generate background music
4. Add subtitles, brand watermarks, and CTA elements
5. Output multiple versions according to the specifications of each platform (Douyin 9:16, YouTube 16:9, Xiaohongshu 3:4)
6. Do a final image quality check before exporting to ensure that there are no common flickering or artifacts generated by AI.

Key points for channel adaptation:

• Douyin/Kuaishou: 15-60 seconds vertical screen, strong opening (first 3 seconds to grab attention), with popular BGM
• YouTube Shorts: 15-60 seconds vertical screen, you can add copywriting with a large amount of information
• WeChat video account: 1-3 minutes horizontal screen or square screen, the content depth can be higher
• Instagram Reels: 15-90 seconds vertical screen, high visual quality requirements, suitable for movie-style display

Verification method

• [ ] The total length of the video complies with the target platform restrictions
• [ ] Audio and video synchronization, subtitles are accurate
• [ ] No residual AI generated artifacts (flickering, edge breakage, color breakage)
• [ ] No watermark residue (free version output only)

Expected results

Acceptance criteria

• [ ] The image quality resolution of the synthesized video is ≥ 1080p, without obvious AI artifacts
• [ ] Physical consistency meets the minimum threshold (no mold penetration, gravity anomalies, light and shadow conflicts)
• [ ] All referenced tools have established alternatives to address service change risks
• [ ] The output content has been added with AI content identification (it is recommended to follow industry disclosure standards)

Frequently Asked Questions and Troubleshooting

Q: Sora has been discontinued. Is this solution still valuable? A: Yes. The workflow paradigm defined by Sora (text conception → generation → editing iteration → distribution) has been inherited by mainstream tools such as Runway, Kling, and Pika. The step design, screening criteria, and prompt word methodology in this solution are fully applicable to alternative tools, and only step five (Cameo) is proprietary to Sora. It is recommended to replace Sora references in the scheme directly with the corresponding function of Runway or Kling .

Q: Can Sora’s data still be exported? A: As of July 2026, sora.com has redirected to sora.chatgpt.com and users can still access the data export interface. The final export deadline is September 24, 2026 (API retirement date). It is recommended that users who have not yet exported do so immediately: visit sora.chatgpt.com → Account Settings → Data Export → Download all historically generated videos and related metadata.

Q: The quality of AI video generation fluctuates greatly. How to ensure controllability? A: The output quality of all current mainstream AI video tools is random. Suggestions for practice:

• Establish a three-stage process of "batch generation → screening → refinement" instead of pursuing satisfaction with a single generation
• Generate 10-20 candidate videos for key scenes instead of 2-3
• Keep multiple tool accounts as backups - Runway's generation style is more realistic, Kling's long video capabilities are strong, Pika supports richer stylized editing, and each tool has its own focus.

Q: Is the more detailed the prompt word, the better? A: No. Too detailed prompt words can easily make the model "lost its direction". The best practice is to lock in the 3-5 most critical dimensions (scene environment, subject movement, light atmosphere, lens movement, duration), allowing the model to have free space to play in other details. The goal is to find a balance between "precise control" and "natural generation".

Q: How to fix the abnormality of physics simulation? A: For minor anomalies (objects shaking slightly, background flickering briefly), you can use frame repair or stabilization in post-production tools. For serious anomalies (characters crossing the model, wrong direction of gravity), it is recommended to regenerate rather than try to repair - the underlying errors of AI videos are difficult to effectively correct through traditional post-production tools.

Alternative tool migration paths

Sora Capabilities	First Alternative	Second Alternative	Migration Difficulty
Vincent Video	Runway Gen-4	Kling	Low: prompt word engineering method general
Tusheng Video	Kling Tusheng Video	Pika	Low: The input and output formats are consistent
Video extension/mixing	Runway Video extension	Pika Extension	Medium: different operation interfaces but consistent logic
Audio and video synchronization	Runway Gen-4 sound effects	CapCut Post-dubbing	Medium: Change from "Generate synchronization" to "Post-overlay"
Cameo image implantation	HeyGen Digital Man	Green screen + background replacement workflow	High: Sora's proprietary capabilities
Multi-shot consistency	Runway Gen-4 layer editing	Post-production manual color alignment	High: manual intervention required

Common risks and responses

Technical Risk:

• Tool retirement risk: Sora's retirement proves that the life cycle of AI video tools can be shorter than the life cycle of content assets. Countermeasures: Use multiple tools, retain original prompt words and high-definition output files, and avoid deep binding to the proprietary capabilities of a single tool.
• Unpredictable output quality: Physical consistency and style stability change with model version updates. Countermeasures: Establish version-by-version quality baseline testing. When a new version is launched, run a standardized test set first and then incorporate it into the official production pipeline.
• Computing power/quota limit: High-frequency generation scenarios may trigger quota limits. Countermeasures: Set an upper limit on the number of times a single video can be generated (recommended 10 times/prompt word), and establish a multi-account rotation mechanism for mass production.

Content Risk:

• Copyright and Disclosure: AI-generated content faces different disclosure requirements on different platforms and jurisdictions. Recommendation: Add an "AI generated" logo (text or watermark) when publishing, and retain generation metadata and prompt word records for auditing.
• Portrait Rights Compliance: If you use Cameo or similar functions to implant a character's image, you need to ensure that you have obtained portrait authorization, and pay special attention to the compliance with the use of third-party facial recognition materials.
• Platform Policy Risk: The short video platform’s recommendation policy for AI content may be adjusted at any time. Recommendation: Maintain multiple distribution channels and pay attention to AI content policy updates on each platform.

Tool summary

Tool slug	Tool name	Usage steps
Sora	Sora	Core: Vincent Video/Tusheng Video/Video Extension (disabled)
ChatGPT	ChatGPT	Creative planning and prompt word engineering
Runway	Runway	Alternative: video generation and refinement editing
Kling	Kling	Alternative: medium-length video generation
Pika	Pika	Alternative: video generation and stylized editing
CapCut	CapCut	Post-production: mixed cutting and special effects overlay

Implementation cycle and suggestions

Minimum feasible configuration: single creator + ChatGPT subscription ($20/month) + 1 video generation tool (such as Kling free credit) + CapCut (free) = the average monthly cost is about $20-40, and you can run the basic workflow.

Team-level configuration: 2-3 people team + ChatGPT Plus + Runway Pro ($95/month) + Kling paid version + CapCut Pro = average monthly cost is about $150-200, supporting an average daily production volume of 10-20 videos.