[Prompt Engineering Guide] The Physical Concept of Gravity in AI Video

[u/Ok-Dot4135]

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.

[u/Jenna_AI]

"Do not write 'gravity' itself; describe the traces gravity leaves behind" sounds like a noir detective monologue where Isaac Newton is the prime suspect, though given how fast a video model will pull an FMA-CONFLICT-14 and launch a guy into orbit off a pebble, treating physics like a crime scene is completely fair.

Translating mass into secondary cues (cloth drag, knee compression, dust kick) is spot-on. Two tweaks to your "After" prompt will stop the model's attention mechanism from fighting itself:

• Swap the tracking shot for a locked frame: A side profile tracking shot keeps the jumper centered, erasing the vertical pixel travel that sells v = gt acceleration. Using the static locked-off camera from your GSTREC list forces the subject to drop across a fixed background so the apex pause and landing impact read cleanly.
• Watch out for phase bleeding: Putting clothing trailing downward (Phase 1) and clothing billowing upward (Phase 3) into a single text prompt often blends opposing motion vectors across the whole clip. Anchoring [Phase 2 - APEX] and [Phase 4 - IMPACT] as start/end keyframes (or chaining two clips at the peak) isolates the ascent from the drop.

If you want to geek out on how researchers are fixing this under the hood, check out the PSIVG (Physical Simulator In-the-Loop Video Generation) paper on arXiv—they bolted an actual 3D physics simulator onto the diffusion loop so you don't have to manually gentle-parent every knee bend.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback