{"id":57831,"date":"2026-10-07T10:04:38","date_gmt":"2026-10-07T02:04:38","guid":{"rendered":"https:\/\/www.1ai.net\/?p=57831"},"modified":"2026-09-20T14:55:01","modified_gmt":"2026-09-20T06:55:01","slug":"ai%e6%8f%90%e7%a4%ba%e8%af%8d%e5%88%9b%e4%bd%9c%e7%ac%ac%e5%9b%9b%e5%8d%81%e4%ba%94%e8%8a%82%ef%bc%9a%e7%8e%a9%e8%bd%acai%e7%a9%ba%e9%97%b4%e6%8e%a7%e5%88%b6%ef%bc%8c%e5%91%8a","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/57831.html","title":{"rendered":"SECTION 45 OF THE AI TIP: PLAYING THE AI SPACE CONTROL AND TELLING PEOPLE TO CONTROL THEIR ROLE"},"content":{"rendered":"

Although the semantic understanding of an AI tool such as Nano Banana Pro, or dream, is already very strong, \u201cprecision control of space position\u201d remains a pain for many creators. Many professional courses teach you to plan the positions and movements of people using complex coordinate axis systems or nodes, but this is too costly for zero-base or efficient creators\u3002<\/p>\n

IN THIS COURSE, WE WILL RETURN TO THE MOST INTUITIVE AND SIMPLE \u201cSEE-AND-TALK\u201d LOGIC. THROUGH A NO-THRESHOLD TECHNIQUE CALLED THE \u201cVISUAL ANCHORING\u201d THAT REQUIRES SIMPLE DRAWING FRAMES AND ARROWS, COMBINED WITH THE UNDERLYING HINTS, AI WILL BE ABLE TO FOLLOW THROUGH AND POINT TO WHERE. NOT ONLY IS IT ACCURATE TO CONTROL STATIONARY POSITIONS, BUT IT ALSO ALLOWS FOR THE PERFECT PLANNING OF MOTION TRACKS IN THE VIDEO\u3002<\/p>\n

CHAPTER 1: WHY DOES AI ALWAYS DISOBEY? THE BOTTOM LOGIC OF UNCONTROLLABLE SPACE<\/strong><\/p>\n

Before we solve the problem, let's start with a core principle:<\/p>\n

WHY DID YOU WRITE A DETAILED LOCATION DESCRIPTION, BUT IT'S HARD FOR AI TO DO IT EXACTLY<\/p>\n

THIS IS ACTUALLY DETERMINED BY THE CURRENT MAINSTREAM AI'S \u201cPROLIFERATION MODEL\u201d BOTTOM-UP MECHANISM. THE IDEA OF A PROLIFERATION MODEL IS TO MAKE A VERY CLEAR PICTURE FROM A BUNCH OF DISORDERLY NOISES, BASED ON YOUR HINTS. IN THIS NEVER-ENDING PROCESS, AI FOCUSED ITS ATTENTION ON \u201cTHE MATCHING OF ELEMENTS\u201d (E.G., THE QUALITY OF THE IMAGE, THE FACE OF THE PERSON, THE MATERIAL OF THE CLOTHES) RATHER THAN \u201cABSOLUTE COORDINATES OF SPACE\u201d\u3002<\/p>\n

\"2031a357jlvfc00ohd000v9000m2p\"<\/p>\n

THIS LEADS TO THE PHENOMENON THAT THE TEXT MESSAGE IS VERY WEAK WHEN IT CONVEYS THE MESSAGE \u201cSPACE POSITION\u201d. WHEN YOU WRITE DOWN A DESCRIPTION OF \"RUN FROM LEFT TO RIGHT\" WITH STRONG SPATIAL PROPERTIES, AI CAN EASILY LOSE IT DURING NOISE REDUCTION OR BE COVERED BY OTHER ELEMENTS WITH HIGHER WEIGHTS\u3002<\/p>\n

AS A RESULT, IT IS OFTEN UNWELCOME TO LIMIT POSITIONS BY WRITING A COMPLEX SET OF HINTS. WE NEED A SIGNAL STRONGER THAN WORDS TO GUIDE AI<\/p>\n

That's the image itself\u3002
\n
\nChapter II: De-dimensional strikes - the principle of \u201cvisual anchor\u201d control<\/strong><\/p>\n

SINCE AI'S \"SPACE PERCEPTION\" OF TEXT IS WEAK, WE'LL SHOW IT DIRECTLY. THIS IS THE CORE METHOD THAT WE HAVE TODAY \u2014 THE VISUAL MOORING\u3002<\/p>\n

THE WHOLE OPERATING PROCESS CAN BE SIMPLIFIED INTO THREE STEPS, AND THE APPROACH APPLIES TO ALL AI TOOLS CURRENTLY AVAILABLE ON THE MARKET THAT SUPPORT THE \"CHART\" OR \"REFERENCE\" FUNCTIONS:<\/p>\n

1. An empty mirror: first, a separate floor map (empty lens) without any description of the person\u3002<\/p>\n

\"61c6a5e5j00tlvfy00ted000v9000hfp\"<\/p>\n

Manual marking: On this bottom map, the position or track of movement of the character is clearly marked with a simple arrow or a frame, using any drawing software (even an amputee editing tool with a mobile phone)\u3002<\/p>\n

\"f45a532ej00tlvga00zod000v90hfp\"<\/p>\n

THE MAP WAS GENERATED: THIS MARKED MAP WAS USED AS A \u201cREFERENCE MAP\u201d AND YOUR CHARACTER WAS FED TO AI\u3002<\/p>\n

\"319fd281jlvgf001ad000in004tp\"<\/p>\n

Enter the prompt word:<\/p>\n

Let the characters in figure I stand in reverse in the position indicated in figure II, and the light merges with nature, with the background, and then removes the red box<\/p>\n

\"9b03d2f0j00tlvgt00w4d000v9000hfp\"<\/p>\n

AND THE DOWNSIDE OF THIS IS THAT YOU HAVE, WITH THE PHYSICAL PIXELS OF THE IMAGE, FORCED THE AREA OF INTEREST GENERATED BY AI. IT'S A LOT MORE PRECISE THAN YOU WRITE 10,000 WORDS FOR POSITION\u3002<\/p>\n

The precision of multiple complex positions<\/strong><\/p>\n

WHAT IF THERE ARE TWO OR MORE PEOPLE IN THE PICTURE WHO HAVE DIFFERENT FEATURES AND POSITIONS? IF IT'S JUST A TWO-COLORED FRAME, AI'S PROBABLY CONFUSED<\/p>\n

We need to use a different colour box or color block\u3002<\/p>\n

\"c3642ba4j00tlvhi00ztd000v9000hfp\"<\/p>\n

WHEN ENTERING A HINT, YOU NEED TO CLEARLY TELL AI WHICH COLOR REPRESENTS WHO:<\/p>\n

\"94ad6153jlhvp004pd000fj00ekp\"<\/p>\n

Tips:<\/p>\n

Sitting figure 2 on the couch of the red box of figure 1, figure 3 on the chair of the green box of figure 1, and the character of figure 4 naturally stands at blue box 1, re-engineered with light, and finally needs to remove the frame<\/p>\n

\"d16f5840j00tlhvi400wjd000v90hfp\"<\/p>\n

Chapter III: Video generation - moving the role on track<\/strong><\/p>\n

Step 1: Mark the track<\/p>\n

On a well-created blank mirror map, the movement trajectory of the person is shown directly on the map\u3002<\/p>\n

\"7e40776cj00tlhvie00v1d000v9000hfp\"<\/p>\n

Step 2: Enter the cue word<\/p>\n

Tips:<\/p>\n

The characters in the figure follow the red arrow's trajectory and do not have a red line in the image<\/p>\n

HERE'S ONE THING THAT NEEDS SPECIAL ATTENTION, WHICH IS THAT WE HAVE TO GET AI OUT OF OUR BOX WHEN IT COMES TO IT (E.G., NOT TO CREATE LINES AND FRAMES IN NEGATIVE HINTS). OTHERWISE, AI WILL GENERATE ARROWS AND FRAMES INTO THE FINAL VIDEO\u3002
\n
\nChapter 4: Progress Details - Color Segregation for Multi-role Movements<\/strong><\/p>\n

WHEN THERE IS NOT ONLY ONE PERSON IN THE PICTURE, BUT TWO OR THREE PLAYERS ARE IN DIFFERENT MOVEMENTS, AI IS EXTREMELY CONFUSED, LEADING ZHANG SAN TO LI SI'S TRAJECTORY\u3002<\/p>\n

At this point, the \u201cdiscretion of colours\u201d becomes particularly critical\u3002<\/p>\n

\"159144e8jlvj90rwd000v90hfp\"<\/p>\n

If you want the green man to go right, the red man to the left, the blue man to stay\u3002<\/p>\n

On the reference diagram, the track is marked with arrows and numbers\u3002<\/p>\n

Enter the prompt word:<\/p>\n

There are three figures in the figure, red, green, blue, green, moving from one to two by arrow, red, moving from one to two by arrow, blue, standing still and watching. Do not display arrows in the video<\/p>\n

Chapter V: Summary and Creative Knowledge<\/strong><\/p>\n

In retrospect, this seemingly simple \u201cframework\u201d approach actually implies the most efficient wisdom of human collaboration. We don't have to explore complex nodes or learn how to enter a three-dimensional system\u3002<\/p>\n

Whether it is Nano Banana Pro, who uses the primary image, or the coven and conch, which are the main attacks, the methodology based on the visual anchor is common. With this technique in hand, the control of the whole picture and the whole video will be firmly in your hands, significantly reducing the pass rate. I want you to open up the AI tools you've got, and try this precision control<\/p>","protected":false},"excerpt":{"rendered":"

Although the semantic understanding of an AI tool such as Nano Banana Pro, or dream, is already very strong, \u201cprecision control of space position\u201d remains a pain for many creators. Many professional courses teach you to plan the positions and movements of people using complex coordinate axis systems or nodes, but this is too costly for zero-base or efficient creators. In this course, we will return to the most intuitive and simple \u201csee-and-talk\u201d logic. Through a no-threshold technique called the \u201cvisual anchoring\u201d that requires simple drawing frames and arrows, combined with the underlying hints, AI will be able to follow through and point to where. Not only is it accurate to control stationary positions, but it also allows for the perfect planning of motion tracks in the video. Chapter 1: Why does AI always disobey? Discover the secret<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[149,144],"tags":[802,3149,956,5321,192,6481],"collection":[],"class_list":{"0":"post-57831","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"hentry","6":"category-jiaocheng","7":"category-baike","8":"tag-ai","12":"tag-prompt","13":"tag-6481"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/57831","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=57831"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/57831\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=57831"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=57831"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=57831"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=57831"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}