4.2.3 Dynamic Scene Generation and Integration

AI MONSTER's technology allows for the deep integration of monsters with their environments, creating cohesive and immersive scenes.

Procedural Environment Generation
AI-driven creation of entire ecosystems and habitats suited to the monsters.
Dynamic adjustment of environments based on monster characteristics and story requirements.
Lighting and Atmosphere Adaptation
Automatic adjustment of scene lighting to enhance the mood and highlight monster features.
Generation of atmospheric effects (fog, particles, etc.) that interact realistically with monsters.
Composite Shot Optimization
AI analysis of live-action footage to determine optimal monster placement and interaction.
Real-time adjustment of monster renders to match plate photography lighting and camera movement.

Example: AI-Powered Scene Composition System

import cv2
import numpy as np
from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation

class AISceneCompositor:
    def __init__(self):
        self.processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640")
        self.model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640")

    def analyze_plate(self, image_path):
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        inputs = self.processor(images=image, return_tensors="pt")
        outputs = self.model(**inputs)
        logits = outputs.logits.squeeze()
        segmentation = logits.argmax(dim=0).numpy()
        return image, segmentation

    def find_monster_placement(self, segmentation, monster_size):
        # Simplified placement strategy - find the largest non-sky area
        ground = (segmentation != self.model.config.id2label['sky'])
        kernel = np.ones((monster_size, monster_size), np.uint8)
        possible_placements = cv2.dilate(ground.astype(np.uint8), kernel)
        best_location = np.unravel_index(possible_placements.argmax(), possible_placements.shape)
        return best_location

    def adjust_monster_lighting(self, monster_render, plate_image, location):
        # Simplified lighting adjustment - match average color around placement
        y, x = location
        surrounding = plate_image[y-50:y+50, x-50:x+50]
        avg_color = surrounding.mean(axis=(0, 1))
        adjusted_render = monster_render * avg_color / 255
        return adjusted_render.astype(np.uint8)

    def composite_shot(self, plate_path, monster_render_path):
        plate, segmentation = self.analyze_plate(plate_path)
        monster = cv2.imread(monster_render_path, cv2.IMREAD_UNCHANGED)
        
        placement = self.find_monster_placement(segmentation, monster.shape[0])
        adjusted_monster = self.adjust_monster_lighting(monster, plate, placement)
        
        y, x = placement
        h, w = monster.shape[:2]
        roi = plate[y:y+h, x:x+w]
        
        # Simple alpha compositing
        alpha = adjusted_monster[:, :, 3] / 255.0
        for c in range(3):
            roi[:, :, c] = roi[:, :, c] * (1 - alpha) + adjusted_monster[:, :, c] * alpha
        
        plate[y:y+h, x:x+w] = roi
        return plate

# Usage
compositor = AISceneCompositor()
composite = compositor.composite_shot("beach_scene.jpg", "sea_monster_render.png")
cv2.imwrite("final_composite.jpg", cv2.cvtColor(composite, cv2.COLOR_RGB2BGR))

Previous4.2.2 AI-Driven Simulations for Enhanced Visual Effects Next4.2.4 Workflow Integration and Production Efficiency

Last updated 2 months ago

import cv2 import numpy as np from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation class AISceneCompositor: def __init__(self): self.processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640") self.model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640") def analyze_plate(self, image_path): image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) inputs = self.processor(images=image, return_tensors="pt") outputs = self.model(**inputs) logits = outputs.logits.squeeze() segmentation = logits.argmax(dim=0).numpy() return image, segmentation def find_monster_placement(self, segmentation, monster_size): # Simplified placement strategy - find the largest non-sky area ground = (segmentation != self.model.config.id2label['sky']) kernel = np.ones((monster_size, monster_size), np.uint8) possible_placements = cv2.dilate(ground.astype(np.uint8), kernel) best_location = np.unravel_index(possible_placements.argmax(), possible_placements.shape) return best_location def adjust_monster_lighting(self, monster_render, plate_image, location): # Simplified lighting adjustment - match average color around placement y, x = location surrounding = plate_image[y-50:y+50, x-50:x+50] avg_color = surrounding.mean(axis=(0, 1)) adjusted_render = monster_render * avg_color / 255 return adjusted_render.astype(np.uint8) def composite_shot(self, plate_path, monster_render_path): plate, segmentation = self.analyze_plate(plate_path) monster = cv2.imread(monster_render_path, cv2.IMREAD_UNCHANGED) placement = self.find_monster_placement(segmentation, monster.shape[0]) adjusted_monster = self.adjust_monster_lighting(monster, plate, placement) y, x = placement h, w = monster.shape[:2] roi = plate[y:y+h, x:x+w] # Simple alpha compositing alpha = adjusted_monster[:, :, 3] / 255.0 for c in range(3): roi[:, :, c] = roi[:, :, c] * (1 - alpha) + adjusted_monster[:, :, c] * alpha plate[y:y+h, x:x+w] = roi return plate # Usage compositor = AISceneCompositor() composite = compositor.composite_shot("beach_scene.jpg", "sea_monster_render.png") cv2.imwrite("final_composite.jpg", cv2.cvtColor(composite, cv2.COLOR_RGB2BGR))