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<\/p>\nTHIS IS THE SPRING OF 2026, AND THE THRESHOLD FOR AI TO GENERATE VIDEO IS ALREADY APPALLINGLY LOW. YOU'RE GOING TO OPEN A PHANTOM OR DREAM AI, AND YOU'RE GOING TO ENTER A SEEMINGLY PERFECT HINT, AND YOU'RE LOOKING TO PRODUCE A HOLLYWOOD-LEVEL CHASE\u3002<\/p>\n
However, the image on the screen is that the main character's hands and feet are held by invisible threads, walking like slips, facial expressions and physical movements as if they were two different time frames\u3002<\/p>\n
YOU MIGHT BLAME THE MODEL FOR NOT WORKING. THE PARAMETERS ARE NOT RIGHT. HOWEVER, THE DATA SHOW THAT THE FAILURE OF 90% STEMS FROM YOUR HABIT OF WRITING ACTIONS AS \u201cLISTS\u201d\u3002<\/p>\n
WE USED TO USE THE LINEAR LOGIC OF HUMANS TO DIRECT AI: \u201cHE STOOD UP AND THEN TOOK TWO STEPS, AND THEN LOOKED BACK AND DREW HIS GUN.\u201d<\/p>\n
BUT REMEMBER THIS: IN AI'S EYES, THE VERB IS NOT \u201cTHE STATE OF THE MOMENT\u201d, BUT IS A STATIC \u201cTARGET STATE\u201d. WHEN YOU PUT A BUNCH OF VERBS INTO THE HINT BOX, AI SEES NOT THE STORY, BUT THE ORDER TO FIGHT EACH OTHER\u3002<\/p>\n
TODAY, TOGETHER WITH THE LATEST GENERATION MODEL LOGIC, WE DISMANTLE THREE CORE TECHNIQUES TO TEACH YOU HOW TO RECONSTITUTE THE ACTION LIST AS THE \"STATE FLOW\" THAT AI UNDERSTANDS PERFECTLY\u3002<\/p>\n
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Chapter I: Rejection of verb stacking and remodelling of the \u201cmotion mode\u201d<\/p>\n 1.1 The verb trap<\/strong><\/p>\n A lot of people write verbs like scripts\u3002<\/p>\n \"A man runs, jumps over a barrier, and rolls on the ground.\" I'm not sure<\/p>\n In a tool with a strong physical model such as a coven or a conch, this line of hints triggers a disaster. The model attempts to present the characteristics of \u201crun\u201d, \u201cjump\u201d and \u201croll\u201d at the same time in a short period of time, resulting in people sliding in the air in a distorted position and dancing in limbs\u3002<\/p>\n This is because the verb often represents a strong geometric shift in the hints. The stacking of verbs is creating a conflict of command\u3002<\/p>\n 1.2 Techniques I: Reduce verbs and add \"modes\"<\/strong><\/p>\n The right thing to do is to make a deduction. Instead of stacking three verbs, a core verb should be retained and its rhythm, gravity and state should be limited by a large number of verbs\u3002<\/p>\n The word Manner is the concept of gold in the metaphor project. It tells AI how to move, not what to move\u3002<\/p>\n Optimal thinking: If you want to show a tense flight, do not write \"run, hide, turn back.\" Core action: Springing (sprinting)<\/p>\n Hesitant steps 1.3 Field case: Nano Banana Pro presentation<\/strong><\/p>\n Assuming that we are going to generate a forest escape video of a suspicious film, the natural environment is much higher than the neon light for light and physical collision\u3002<\/p>\n Picture Cue Words:<\/p>\n movie picture, nordic black skepticism. a woman dressed in a dark windcoat passed through the thick, misted pine forest at dusk, in despair and at full speed. she has a heavy forward graft (showing a \u201cfront impulse\u201d) which appears to have lost balance and to have some lull on the uneven mud floor. the mud was splattered around her boots. her hands were blurry and she was pulling out the pine branch of the road. cold blue and low saturation gray. high contrasts, heavy atmospheric fog, fuzzy dynamics. 35mm film shoot, particle-mass\u3002<\/p>\n Failed hint<\/p>\n A woman runs fast in the future.<\/p>\n A woman runs fast in the woods. I'm not sure<\/p>\n (Issues: The picture is usually stable, but it is often like a picture of a tourist whose character is \u201cfloating\u201d in the background and whose lack of speed is a sense of oppression. I'm not sure<\/p>\n Success indicators:<\/p>\n Body leaving behind at 45 months, and we're flying all the way out of the bubbles<\/p>\n Parsing: We only keep a core verb, Springing, and then we strengthen it with the physical state. It's like adding \"gravity parameters\" to the physical engine of AI, and it's more film-like than a simple \"Runs fast\" image\u3002<\/p>\n Chapter II: A \u201cmechanical sense of farewell\u201d, creating the primary sublevel of action<\/p>\n 2.1 \u201cCIVIL WAR\u201d OF PHYSICAL CONTROL: WHY ARE YOUR AI PUPPETS<\/strong><\/p>\n Now the video model is rarely accompanied by a low-level error. But do you realize that when you create complex actions, people often have a weird mechanical sense<\/p>\n FOR EXAMPLE, YOU WANT TO LOOK BACK WHILE RUNNING. IN THE VIDEO PRODUCED BY AI, THE CHARACTER'S LEGS ARE RUNNING AND HIS HEAD IS SPINNING, BUT HIS SHOULDERS AND TORSO ARE AS STIFF AS WELDED. IT LOOKS LIKE THE UPPER HALF OF A WALKING MODEL THAT WAS FORCED TO ANOTHER ANGLE\u3002<\/p>\n THIS IS BECAUSE, IN THE CALCULATION LOGIC OF AI, IF YOU FLATTEN THE ACTION, IT WILL TRY TO DISTRIBUTE IT EVENLY. IT IS DOING ITS BEST TO RUN, AND IT IS DOING ITS BEST TO TURN BACK\u3002<\/p>\n The result is that all parts of the body are \u201cshow each other\u201d and lose the overall coordination of the core of human action \u2014 it's like a wooden wiring, with two people pulling the legs and the nozzles, without muscles\u3002<\/p>\n 2.2 Techniques II: The Anchor Method<\/strong><\/p>\n TO ADDRESS THE \u201cMECHANICAL SENSE\u201d, AI MUST UNDERSTAND THE PRIMARY RELATIONSHIP OF THE ACTION\u3002<\/p>\n A natural action hint must contain two levels:<\/p>\n The anchor action (Anchor Action): determines the physical inertia, gravity and overall shift of the body. Usually it's a torso and leg move (e.g. Walker, Sitting, Springing)\u3002<\/p>\n Subordinate action (Satellite Action): A fine tune attached to the anchor. It's usually a head, arm or expression (e.g. looking back, Waving, Dringing)\u3002<\/p>\n Core law: The image is only natural if the subordinate action follows the rhythm of the anchor action\u3002<\/p>\n 2.3 Cases of combat:<\/strong><\/p>\n Let's do a classic movie shot: \"Look back in the walk.\"\u3002<\/p>\n The wrong \"paraphrase\" is:<\/p>\n He is lecturing his bodies forwards up.<\/p>\n AI UNDERSTOOD LOGIC: AI RECEIVED FOUR PARALLEL INSTRUCTIONS: 1. JUMP, 2. WRESTLING, 3. STOP, 4. SHAKE HANDS. IT'LL TRY TO COLLIDE THE FOUR ACTIONS. THE RESULT IS LIKELY TO BE A RIGID BODY IN THE AIR, FOLLOWED BY \u201cSHOW\u201d IMAGES OF THE CORRESPONDING POSITION, LACKING A FLUID AND TENSION DRIVEN BY THE CORE MUSCLE MASS IN THE TARGET PICTURE\u3002<\/p>\n Correct anchor locking<\/p>\n An expose, powerful force destroyed by intense torso twist and core alignment.<\/p>\n AI UNDERSTANDS THE LOGIC: AI UNDERSTANDS: \"OH, THE HEAD OF THE MOVE IS `THE EXPLOSIVE LEAP OF THE TORSO. ' IN ORDER TO DO THE JOB THE BOSS TOLD YOU, THE LEGS HAD TO BE PACKED (OR HIT AGAINST THE WALL) AND THE HANDS HAD TO BE THROWN BACK (OR THE BALANCE HAD TO BE LOST).\u201d SO THE PICTURE THAT COMES OUT, EVERY MUSCLE IS GOING IN A REASONABLE DIRECTION, AND IT'S PERFECT TO RECREATE THE BIG FILM YOU WANT WITH A SENSE OF TENSION AND COORDINATION\u3002<\/p>\n Chapter 3: Breaking the linear time and translating the \"sequence\" into \"state constraint\"<\/p>\n 3.1 AI HAS NO SENSE OF TIME<\/strong><\/p>\n This is the hardest thing for starters to understand. When you write in the hint:<\/p>\n \"The man eats the cake, then smiles, and finally stands up.\" I'm not sure<\/p>\n You're dealing with a no-time axis<\/p>\n The conceptual model speaks of logic. The current proliferation model and the DiT model are essentially generating a continuous noise-to-noise process rather than implementing a script code\u3002<\/p>\n When you use the words \"then\" and \"After\" (after), AI tends to integrate these three states into the picture. As a result, a man's mouth is covered in cakes, with a weird smile on his face while he's holding a half a stop and a half a seat\u3002<\/p>\n 3.2 Tactic III: Status snapshot<\/strong><\/p>\n Instead of describing the order in which time passes, describe the specific state in which the action occurs\u3002<\/p>\n AI IS GOOD AT FINDING THE MOST RATIONAL POSTURES UNDER CERTAIN CONDITIONS. WE NEED TO TRANSLATE THE STORY INTO THE STATE OF THE SCENE\u3002<\/p>\n 3.3 Cases of combat:<\/strong><\/p>\n Let's say the script is: the main character finishes his last sip and throws his cup over the table\u3002<\/p>\n Linear:<\/p>\n He wins the drink, then slams the glass on the table often.<\/p>\n State-bound writing (translated from the moment it happened):<\/p>\n Section: The movement of action.<\/p>\n SEE? WE DIDN'T WRITE THE \"DRINKING OUT\" MOVE, BUT WE JUST DESCRIBED THE \"EMPTY CUP\" AS BEING \"PRESSED ON THE TABLE.\" AI WILL AUTOMATICALLY MAKE UP FOR THE LOGIC -- SINCE THE CUP JUST FELL AND THE DROPS WERE STILL FLYING, IT MUST HAVE JUST BEEN FINISHED\u3002<\/p>\n By describing the Mid-action State, we tricked AI to create the most intense moment, and the brain automatically helped the audience to complete the consistency\u3002<\/p>\n Chapter 4: Summary<\/strong><\/p>\n BEING AN AI DIRECTOR, NOT A TYPIST<\/p>\n IT'S ONLY WHEN YOU STOP GIVING AN A.S. LIKE A \"FIRST AND THEN\"... AND YOU START TO DESCRIBE THE CHARACTER'S STATE, EMOTIONAL TENSION, MUSCLE TENSION, LIKE A \"HUMAN.\"\u3002<\/p>\n Fragmentation is never a matter of random probabilities, and it must be a hint of a wrong logic\u3002<\/p>\n From today on, check your hint:<\/p>\n How many verbs\u3002<\/p>\n - First grade? Find one of the initiatives, the others to fix\u3002<\/p>\n Do you have time to delete the word \"and then\" and replace it with a description of the current state\u3002<\/p>\n![\"4593ff6cj00tlg8h9000x1d000v90hfp\"](\"https:\/\/www.1ai.net\/wp-content\/uploads\/2026\/09\/4593ff6cj00tlg8h900x1d000v900hfp.jpg\" "\\"4593ff6cj00tlg8h9000x1d000v90hfp\\"")
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\nHeavy breathing
\nWeight Shifting
\nUnbalanced motionum<\/p>\n![\"1be112afj00tlg8hw0165d000v90hfp\"](\"https:\/\/www.1ai.net\/wp-content\/uploads\/2026\/09\/1be112afj00tlg8hw0165d000v900hfp.jpg\" "\\"1be112afj00tlg8hw0165d000v90hfp\\"")
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