4.2.2 AI-Driven Simulations for Enhanced Visual Effects

AI MONSTER's technology extends beyond static design, offering dynamic simulations that bring creatures to life with realistic movements and interactions.

1. Physically-Based Animation

2. AI models trained on real-world physics and animal movements to generate realistic monster animations.

3. Real-time adaptation of monster movements to different environments and scenarios.

4. Crowd Simulation

5. Generate and control large groups of diverse monsters for epic scenes.

6. Each monster in the crowd can have unique behaviors and appearances.

7. Environmental Interaction

8. Simulate how monsters interact with their surroundings, including terrain deformation, fluid dynamics, and destruction effects.

9. Automatic generation of secondary animation effects (e.g., vegetation movement, dust clouds) based on monster actions.

Example: AI-Powered Monster Animation System

import torch
import numpy as np
from transformers import GPT2LMHeadModel, GPT2Tokenizer

class AIMonsterAnimator:
    def __init__(self):
        self.model = GPT2LMHeadModel.from_pretrained("aimonster/gpt2-monster-animation")
        self.tokenizer = GPT2Tokenizer.from_pretrained("aimonster/gpt2-monster-animation")
        self.model.to("cuda")

    def generate_animation_sequence(self, monster_type, action, duration_seconds, fps=24):
        prompt = f"Generate animation keyframes for a {monster_type} performing {action} for {duration_seconds} seconds:"
        input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda")
        
        output = self.model.generate(
            input_ids,
            max_length=500,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            temperature=0.7
        )
        
        keyframes = self.tokenizer.decode(output[0], skip_special_tokens=True)
        return self.parse_keyframes(keyframes, duration_seconds, fps)

    def parse_keyframes(self, keyframes_text, duration_seconds, fps):
        # Parse the generated text into a structured animation data
        # This is a simplified example and would need to be expanded for real use
        lines = keyframes_text.split("\n")
        animation_data = []
        for line in lines:
            if ":" in line:
                time, pose = line.split(":", 1)
                frame = int(float(time) * fps)
                animation_data.append((frame, pose.strip()))
        return animation_data

    def interpolate_animation(self, keyframes, total_frames):
        # Simple linear interpolation between keyframes
        # In a real system, this would use more advanced interpolation methods
        interpolated = np.zeros((total_frames, 3))  # Assuming 3D positions for simplicity
        for i in range(len(keyframes) - 1):
            start_frame, start_pose = keyframes[i]
            end_frame, end_pose = keyframes[i+1]
            start_pos = np.array([float(x) for x in start_pose.split()])
            end_pos = np.array([float(x) for x in end_pose.split()])
            for frame in range(start_frame, end_frame):
                t = (frame - start_frame) / (end_frame - start_frame)
                interpolated[frame] = start_pos * (1-t) + end_pos * t
        return interpolated

# Usage
animator = AIMonsterAnimator()
monster_type = "tentacled sea monster"
action = "emerging from the ocean and attacking a ship"
duration = 10  # seconds

keyframes = animator.generate_animation_sequence(monster_type, action, duration)
total_frames = duration * 24  # Assuming 24 fps
animation = animator.interpolate_animation(keyframes, total_frames)

print(f"Generated {len(keyframes)} keyframes for a {duration}-second animation")
print(f"Interpolated into {total_frames} frames of animation data")