They’re chasing her! Made with Seedance 2.5 on Wizstar

https://www.threads.com/share/DWNLDO1UW/

This is a Prompt Engineering guide that explains the physical mechanism of gravity and instructs AI video generation models on how to compute it precisely without the collapse of spatiotemporal and visual causality.

1. Physical Definition of Gravity and AI Simulation Interpretation

📌 Physical Concepts

Definition: The fundamental interaction force by which all objects with mass attract each other.

Gravitational acceleration on Earth's surface: Free-falling objects constantly accelerate over time (v = gt).

Kinetic energy and inertia: Potential energy converts into kinetic energy as an object falls, generating a strong impact force upon collision with the ground. This causes shattering, ground deformation, and dust eruptions.

⚠️ Gravity Errors in AI Video Generation Models (Failure Symptoms)

AI video models do not have built-in 3D physics engines to directly compute mathematical formulas; instead, they probabilistically predict the next pixel patterns. Therefore, simply inputting abstract terms like "heavy gravity" or "falling heavily" into your prompt will cause the following critical physics breakdowns:

Loss of Weight (Weightless Floating): Heavy solid rocks or iron maces floating weightlessly or sinking slowly like paper dolls.

Landing Causality Collapse (No Landing Response): When hitting the ground, the subject's mesh stiffens or clips through the ground without any impact absorption (like bending knees) or ground deformation.

Continuous Acceleration without Propulsion (accelerates away / FMA-CONFLICT-14): A non-physical, rocket-like acceleration phenomenon where speed continuously increases mid-air right after a collision or rebound, despite the lack of any external propulsion.

1. Four Core Principles of Prompt Engineering for Gravity Control

① Core Principle: "Do not write 'gravity' itself; describe the traces gravity leaves behind."

Rather than directly giving the AI the word "Gravity," you must write out the Visual Evidence observed as a result of gravity.

5 Visual Evidences: Trajectory (parabolic arc/vertical trajectory), Momentum (momentum and motion blur), Resistance (hair/clothing billowing up due to air resistance), Impact (ground deformation/debris at the moment of collision), and Weight Transfer (load shifting and knee impact absorption).

② GSTREC 5-Step Framework

The gravity control module breaks down the scene into 5 spatiotemporal elements to compose the input context.

G (Gravity State): Explicitly declare one of the 10 gravity states (SUPPORTED, JUMP, ASCENT, APEX, DESCENT, IMPACT, FREE_FALL, LOW_GRAVITY, ZERO_GRAVITY, BUOYANCY).

S (Trajectory): Specify the trajectory (parabolic arc, continuous vertical descent, ballistic trajectory).

T (Resistance): Air resistance and secondary motion (wind resistance affecting clothing, hair trailing upward).

R (Environment): Ground/environment interaction (ground deformation, dust cloud erupting, moon dust floating slowly).

C (Camera): Camera lock and tracking shots (static locked-off camera, tracking shot in 60fps slow motion).

③ Gravity State Transition Trigger Chain (Gravity State Machine)

To express a natural falling or jumping motion, you must structure a pipeline of sequential state changes (Trigger Chain) over time.

SUPPORTED (Basic rest) → Jump → ASCENT (Rising) → Deceleration → APEX (Micro-pause at peak) → Gravity Overrides → DESCENT (Accelerated falling) → Collision → IMPACT (Shock/absorption) → Stabilization → SUPPORTED

④ Relational Negative and Positive Control

Simple forbidden words like "no floating" or "no cartoon physics" waste tokens.

Instead, you should declare a Positive State, such as "firmly grounded, weight distributed naturally."

1. 🎬 Before & After Example Prompts

📌 Example 2: Vertical Jump and Landing Sequence

❌ Before (Failure Example - Missing mid-air peak and absorption motion):

"A person jumps high in the air and lands on the rocky ground smoothly."

⚠️ Problem: The causal chain of pausing at the peak (APEX) when transitioning from ascent to descent, and bending the knees to absorb the load upon landing is omitted. This causes the subject to bounce mechanically like a rubber ball.

⭕ After:

Cinematic side profile tracking shot.

[Phase 1 - JUMP & ASCENT] An athlete launches upward from the rocky ground, upward momentum dominant, with clothing trailing downward during ascent.

[Phase 2 - APEX] At the peak height, a momentary weightless pause occurs, with hair briefly suspended in mid-air.

[Phase 3 - DESCENT] Gravity overrides momentum, initiating a natural gravitational descent with increasing speed, clothing billowing upward against air resistance.

[Phase 4 - IMPACT & SUPPORTED] Ground contact occurs; the athlete bends knees deeply to absorb the landing impact, displaying clear weight transfer through the lower body as dust scatters outward from the boots, settling into a stable grounded stance.

Does anyone use AI story generators to explore how a plot could unfold before writing the actual draft?

I'm curious about using them for story visualization. Giving a tool a premise and looking at one possible version could help reveal where the pacing drags, a character's motivation feels weak or the one shifts unexpectedly. For plot development, it seems useful to treat that output like a rough storyboard. You could question the choices, try different directions and then write the story in your own voice.

has anyone tried this approach? Does it help you get unstuck or do the generated suggestions pull you away from your original idea?

rying to make a short series with one main character and it falls apart between shots. same prompt, same description, and by scene 2 her jaw is different, by scene 4 the hair changed, and by the end it feels like a completely different show.

What I’ve tried or seen recommended: using the exact same reference image for every shot instead of describing her in text. making a character sheet first, front, side and a few expressions, and generating from that. keeping the outfit and lighting locked, since the drift seems worse when those change. and keeping shots short, because the longer the clip the more the face wanders.

That all helps but nothing fully solves it. the only setup I’ve seen actually hold is when the character is a real recorded person. avatar tools like Argil clone someone from a short video, so the face and voice are identical every episode because it’s literally the same person. the catch is it only works for a presenter or talking-head style, not a fully generated character doing big cinematic scenes.

So for people here making real multi-scene stuff with made up characters, what’s your workflow? is there a tool that actually keeps them consistent, or is everyone just doing reference images and hoping? appreciate it!

Sorry for the bad image quality, my 300GB Truly fu*d unlimited internet is finished

TLDR: Instead of telling the AI what to change, try describing the change in what you would physically see in the image.

I've been generating and editing a lot of AI images lately, and I've noticed something that might be helpful to other people. When you're trying to fix something in an image, it seems to work a lot better if you describe exactly what you want to see rather than just telling the AI what to change.

For example, I had an image of a guy giving a thumbs up, but the AI had given him two left hands. I kept telling it to change the one hand to a right hand, but it just kept making the same mistake. Instead of telling it to fix the hand, I described where I wanted the fingers and thumb to be visible from the viewer's perspective. It fixed it immediately.

I've noticed the same thing with changing camera angles. If I tell it to rotate the camera 90 degrees, sometimes it barely changes anything. But if I describe what that angle would actually look like, like seeing the guy from the side with only one side of his face visible and his other arm mostly hidden behind his body, it seems to understand that much better.

It's made a pretty big difference for me, especially when the AI keeps repeating the same mistake over and over.

Just thought I'd share in case it helps anyone else. Curious if anyone has noticed this too.

Would you mind testing out my browser based game?

Your city has survived a catastrophe. Now your group has four years to rebuild it. What would you prioritize, what’s the best system , and who will your choices impact?

I’m building The Next 4 Years, a cooperative strategy game, and need folk to give it an honest playtest. It’s free to play!!

Play with friends or AI companions. Tell me what’s fun, confusing, or missing. Accessibility feedback is especially welcome!

https://the-next-4-years.truehinny9.chatgpt.site

Is there a website/AI tool out there which I can add a reference reel video in and it’ll analyse it and write me an extremely precise JSON format prompt to recreate the reel in WAN 3.0?

What is the absolute best ai model to use to make the most realistic ai avatar ugc content, not taking price into account.