How can generative AI create realistic 3D models?

Furthermore, 3D models play a crucial role in engineering and product design. They enable engineers and designers to prototype and test concepts in a digital environment, reducing the need for physical prototypes and speeding up the development process. This not only saves time and resources but also facilitates more iterative and innovative design approaches. In the medical field, 3D models are used for various purposes, such as visualizing complex anatomical structures, planning surgeries, and creating custom implants or prosthetics tailored to a patient’s unique anatomy. This personalized approach enhances patient care and surgical precision.

Digital models and simulations can be achieved by good data collection aggregated into tools and LLMs/AGI to show us things we simply cannot see on our own. ???? We Built the physical environment, the digital twins age is now - Everything is data ??

3D models are digital representations of objects or environments, created using specialized software. They consist of geometric data, textures, and sometimes animations, enabling realistic visualization, simulation, and interaction within digital spaces, essential for many fields including gaming, architecture, and film.

Generative AI being used for generating 3D models is also very crucial in the field of Synthetic Data. While Real Data is important, at the same time it is also very expensive to acquire and annotate them. In this regards, Synthetic Data is cost effective and also allows us to design a scenario in any way we want. E.g. if we want to have a pedestrian crossing a highway (corner case) it is difficult to capture such a case in real data where in synthetic data it could be achieved easily. Synthetic data involves creating a 3D world where one can perform simulations. Creating a 3D scene requires a lot of 3D assets and hence it would be extremely beneficial to generate 3D assets using Gen AI which will be extremely cost effective.

The representation of 3D is the biggest challenge for AI to learn. The data representation by polygon Mesh is variable and discrete. Thus a challenge for ANN'S. Alternative approaches of representation should be explored which provides better predictability for same, for example a voxels representation as input or 2D image.

Generative AI, harnessing algorithms like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), can create 3D models. It learns from existing data, understanding shapes and structures to generate new models. The process begins by training on a dataset of 3D models, learning to mimic the underlying distributions. Post-training, the AI can generate new models by sampling from the learned distribution. GANs, for instance, use two networks: a generator creating new models, and a discriminator evaluating them. Through iterative training, the generator improves, producing realistic 3D models. This facilitates automatic generation of high-quality 3D content, useful in design, gaming, and other domains.

For generating the 3D data, two important factors for a polygon mesh that needs to be generated are predicted vertices and predicted faces. This two data together creates Mesh or voxels. The faces are dependent on vertices, a model that can predict the polygon faces given vertices as input with 3 dimensional data needs to be created. This models can be trained by existing data 3D data. Transformers nowadays is default but not really efficient google PolyGen is a good example.

Generative AI can create realistic 3D models by using a variety of techniques, including: Generative adversarial networks (GANs): GANs are a type of neural network that are trained on two sets of data: a real dataset and a fake dataset. The GAN learns to generate fake data that is indistinguishable from the real data. This can be used to generate realistic 3D models by training a GAN on a dataset of real 3D models. Ex:GauGAN Variational autoencoders (VAEs): VAEs are a type of neural network that are used to learn the latent space of a dataset. The latent space is a lower-dimensional representation of the data that captures the essential features of the data. VAEs can be used to generate realistic 3D models by sampling from the latent space.

Generative AI, such as Generative Adversarial Networks (GANs), can be integrated with 3D modeling software to allow users to input creative prompts, which the AI model interprets to generate basic 3D models. NeRF algorithms can be used to make the models more realistic. The models can then be converted into photorealistic images or animations using rendering software. Users can provide creative prompts to AI systems, which interpret these prompts to produce initial 3D model designs, which can be further fine tuned to meet specific needs. There is already some good work in progress like Nvidia GET3D and Luma AI, where the integration of Generative AI with 3D modeling software is giving good results.

In this context, I would like to highlight responsible AI and ethical concerns. Models can generate results that can be misused. It's a balance of innovation and regularization.

Implicit generative models (IGMs) learn to generate data by mapping random noise to data points, without explicitly learning the data distribution. Examples include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GANs consist of a generator and a discriminator, working adversarially to produce realistic data. The generator creates data, while the discriminator evaluates it, helping the generator to improve over time. VAEs, on the other hand, are probabilistic and learn a mapping of data to a lower-dimensional latent space, enabling the generation of new data points by sampling from this space. Both models are powerful tools for tasks like image generation, denoising, and creating realistic 3D models.

As far as realism is concerned there are multiple factors to be considered like movement across 3D plane needs to be predicted with elasticity on object body, Optical objects reflecting light should be able to create and predict reflections on surfaces. The challenge would be to first collect the data , 3 D Games with Ray tracing enabled can be employed to generate training data. Transformers again are good options can, the 3 D image creation can be broken into smaller sub images and transformer can be employed. A 3dD natural scenery generation may require to divide generation into even smaller sub images. If we are using 2D image as input the overlap between sub image can should also be provided as input.

Explicit generative models, also known as explicit density models, are a class of generative models that aim to explicitly model the probability distribution of the data they are generating. These models are designed to provide a clear and interpretable representation of the data's underlying structure. Explicit generative models contrast with implicit generative models, which focus on generating data without explicitly modeling its probability distribution. In addition to Variational Autoencoders (VAEs) and Neural Radiance Fields (NeRF), there are several other examples of explicit generative models. These include: Mixture Density Networks(MDNs), Auto-Regressive Models, Auto-Regressive Models,PixelCNN & PixelRNN,Normalizing Flows, GMMs,etc

Some experiences from VAE is that training stability is what needs to considered; use batch normalization and dropout, and adaptive learning rate methods.

Generative AI has been increasingly utilized for 3D modeling, offering automated and enhanced workflows. Various tools and frameworks support this endeavor: NVIDIA Omniverse: A multi-GPU real-time simulation and collaboration platform for 3D simulation and photorealistic rendering, often employed for generative design. Runway ML: Provides easy access to generative algorithms for creating 3D models among other media types. Artbreeder: An online platform blending images and 3D models using generative techniques. Google's DeepDream: Though primarily for images, creative applications have extended its use to 3D models. Blender: With scripting capabilities, Blender can be used in tandem with generative algorithms for 3D modeling.

PolyGen is transformer based Model developed by Google deep mind. Creates 3D images from 2d images or input voxels. Outputs polygon Mesh.

Generative AI tools are like magic for creating cool 3D models. StyleGAN is amazing for making lifelike 2D images of faces and animals. MeshGAN is another tool that turns 2D pictures into detailed 3D models. ShapeNet is like a giant collection of 3D shapes to teach these tools, and PyTorch3D is a toolkit to make it all work smoothly with PyTorch. You can find these tools online or download them for your 3D adventures!

Security Concern on Generated 3D model: Unauthorized Access and Theft: Generative 3D modeling tools and the models they create could be targeted by cyber-attacks. Unauthorized access could lead to theft of proprietary designs. Intellectual Property (IP) Protection: Ensuring IP rights becomes challenging, especially when generative AI creates models that closely resemble existing, copyrighted designs. Malicious Usage: Generated 3D models could be used maliciously, e.g., creating realistic models for deepfakes, counterfeit products, or unauthorized reproductions of protected designs.

Be aware of any legal or intellectual property issues related to the 3D models you generate. Ensure that you have the necessary rights or permissions to use and distribute the generated content.

The model complexity can be huge and thus its important to define domain within 3D, what exactly is definition of realism, how intense the scenes can be in terms of details. Choosing different models for different use case and bringing final scene together may be better approach. Model generating background can be different and foreground can be different as details of foreground may be more important t han background. Model generating objects can be different and are simpler. This all can be Pipelined together and brought into single frame by context of 2D image. The aggregating model would need to fix the edges while mixing and context from 2D image would serve the purpose. Similarly training data is needed based on the approach.

Areas that still require some development in Gen.AI rendering are, for example, the correct creation of shadows and wind effects. I see an analogy with game engines where these features already exist, so it's only a matter of time before they become available in the models.