3D image generation in Computer Vision with implementation | Computer Vision

In artificial intelligence, image generation is a trending topic, you may know or daily use chat gpt or Midjourny, it helps your business robustly. Image generation may have several types, such as text-to-image, image-to-image, 3D image generation and artistic style transfer. In this article, we will discuss basic image generation, types of image generation, and a coding implementation for image generation. So, let's deep drive onto it.

Image generation is the process of creating new images from scratch using machine learning or artificial intelligence (AI) methods. This expertise is crucial in several industries, including computer graphics, the arts, entertainment, and more. It allows for the development of realistic or creative images, data augmentation for deep learning model training, and even the generation of images based on particular characteristics or aesthetic preferences.

What is 3D Image Generation?

A technique known as 3D image generation converts two-dimensional (2D) flat images into three-dimensional (3D) representations. Essentially, it enables computers to give objects perspective, depth, and structure to make them appear more realistic and lifelike.

Complex algorithms are used in computer vision to generate 3D images by analyzing 2D images and determining the depth and spatial relationships of various scene elements. This procedure contributes to the creation of a 3D model that resembles real-world objects and can be viewed from different perspectives.

Types of image generation techniques:

There are several types of image generation techniques. Here are some of what we discussed.

Generative Adversarial Networks (GANs): GANs consist of a generator and a discriminator network. The generator creates images, while the discriminator distinguishes between real and generated images. GANs are known for their ability to produce high-quality and diverse images.

Variational Autoencoders (VAEs): VAEs are probabilistic models that generate images by sampling from a learned latent space. They are useful for generating images with controlled attributes and have applications in image reconstruction.

Auto-Regressive Models: Auto-regressive models generate images one pixel at a time, often using recurrent neural networks (RNNs) or transformers. Pixel values are predicted sequentially based on previous pixels.

Flow-Based Models: Flow-based models transform a simple distribution into a complex data distribution, allowing for image generation. They are known for their invertibility and can be used for generative tasks.

Implementation Part:

Here, we are implementing a basic image generation using an adversarial network. You will get the full project code on Google Colab. Firstly,  we import the necessary library for our implementation. 

Import library

import tensorflow as tf
from tensorflow.keras import layers, models, datasets
import numpy as np

Model define

In this code, we define the architecture of the generator model. It takes the latent_dim as an input, which represents the dimension of the input noise vector. Here are two models: the generator model and the discrimination model.

# Define the generator model
# Define the generator model
def build_generator(latent_dim):
# Create a sequential model (a linear stack of layers)
model = models.Sequential()
    
# Add a dense layer with input dimension as latent_dim
model.add(layers.Dense(7 * 7 * 256, input_dim=latent_dim))
    
# Reshape the output to a 7x7x256 tensor
model.add(layers.Reshape((7, 7, 256)))
    
# Add a transposed convolutional layer with 128 filters, a 4x4 kernel, and 2x2 strides
model.add(layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same'))
    
 # Add batch normalization to stabilize training
 model.add(layers.BatchNormalization())
    
 # Add LeakyReLU activation with a small slope (alpha=0.2)
  model.add(layers.LeakyReLU(alpha=0.2))
    
 # Add another transposed convolutional layer with 64 filters, 4x4 kernel, and 2x2 strides
 model.add(layers.Conv2DTranspose(64, (4, 4), strides=(2, 2), padding='same'))
    
# Add batch normalization
 model.add(layers.BatchNormalization())
    
# Add LeakyReLU activation
 model.add(layers.LeakyReLU(alpha=0.2))
    
# Add a final transposed convolutional layer with 1 filter, a 7x7 kernel, and sigmoid #activation
    
model.add(layers.Conv2DTranspose(1, (7, 7), activation='sigmoid', padding='same'))
    
# Return the generator model
 return model

# Define the discriminator model
def build_discriminator(img_shape):
  # Create a sequential model
  model = models.Sequential() 
  # Add a convolutional layer with 64 filters, 3x3 kernel, 2x2 strides, and input shape img_shape
  model.add(layers.Conv2D(64, (3, 3), strides=(2, 2), padding='same', input_shape=img_shape)) 
  # Add LeakyReLU activation
  model.add(layers.LeakyReLU(alpha=0.2))
  # Add dropout to prevent overfitting
  model.add(layers.Dropout(0.4))    
  # Add another convolutional layer with 128 filters, 3x3 kernel, and 2x2 strides
  model.add(layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same'))
  # Add LeakyReLU activation
  model.add(layers.LeakyReLU(alpha=0.2))  
  # Add dropout
  model.add(layers.Dropout(0.4)) 
  # Flatten the output to a 1D vector
  model.add(layers.Flatten())   
  # Add a dense layer with 1 neuron and sigmoid activation to classify real or fake
  model.add(layers.Dense(1, activation='sigmoid'))
  # Return the discriminator model
  return model

# Define the GAN model
def build_gan(generator, discriminator):
    discriminator.trainable = False
    model = models.Sequential()
    model.add(generator)
    model.add(discriminator)
    return model


# Define hyperparameters
latent_dim = 100
img_shape = (28, 28, 1)
batch_size = 64
epochs = 100


# Load and preprocess the dataset (MNIST)
(train_images, _), (_, _) = datasets.mnist.load_data()
train_images = train_images / 127.5 - 1.0
train_images = np.expand_dims(train_images, axis=-1)


# Build and compile the discriminator
discriminator = build_discriminator(img_shape)
discriminator.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Build and compile the generator
generator = build_generator(latent_dim)
discriminator.trainable = False
gan = build_gan(generator, discriminator)
gan.compile(loss='binary_crossentropy', optimizer='adam')
# Training loop
# Training loop
for epoch in range(epochs):
 # Generate random noise samples
 noise = np.random.normal(0, 1, (batch_size, latent_dim))
 # Generate fake images from noise
 generated_images = generator.predict(noise)
 # Select a random batch of real images from the dataset
idx = np.random.randint(0, train_images.shape[0], batch_size)
real_images = train_images[idx]
# Labels for the real and fake images
real_labels = np.ones((batch_size, 1))
fake_labels = np.zeros((batch_size, 1))


# Train the discriminator on real and fake images
d_loss_real = discriminator.train_on_batch(real_images, real_labels)
d_loss_fake = discriminator.train_on_batch(generated_images, fake_labels)
# Calculate the total discriminator loss
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# Generate new random noise samples
noise = np.random.normal(0, 1, (batch_size, latent_dim))
# Labels for the generator (tricking the discriminator)
 valid_labels = np.ones((batch_size, 1))
# Train the generator to fool the discriminator
  g_loss = gan.train_on_batch(noise, valid_labels)
 # Print progress
  print(f"Epoch {epoch}/{epochs}, D Loss: {d_loss[0]}, G Loss: {g_loss}")
# Save generated images at specified intervals
   if epoch % 100 == 0:
    generated_image = generator.predict(np.random.normal(0, 1, (1, latent_dim)))
        generated_image = 0.5 * generated_image + 0.5

Challenges in Image Generation

Image generation is a complex task. There are challenges faced by image generation. There are some common mode collapses, Balancing Exploration and Exploitation. Mode collapse happens when a GAN produces a small number of visually similar images rather than fully capturing the diversity of the training data.    

Recent Advancements and Future Research Directions

Recent improvements in picture creation show a lot of potential. Self-attention methods have been used to improve long-range dependencies in images. Researchers are also diligently attempting to produce more diverse and innovative images, preventing mode collapse by encouraging models to explore the huge space of data distributions.

3D image generation is used for multiple tasks day by day, and new fields are emerging from it. In this article, we tried to cover basic image generation, types of image generation, and an implementation. A single tutorial can only describe a partial part of image generation. You can learn more from here.