Neural networks in 3D and animation creation – changes 2025

Artificial intelligence (AI) has dramatically changed the approach to creating models, animations, and textures, significantly speeding up processes that previously took weeks. Now, the opportunities available to designers, animators, and studios are comparable to the technological revolution in cinema of the past century.

These innovations not only save time and resources but also open doors for creative expression.

A turning point has been the development of generative neural networks capable of creating complex 3D models and realistic animations from text descriptions. For many business sectors — from game development to virtual reality — this means lower entry barriers and the ability to focus on the artistic aspect.

Generating 3D models using neural networks

Generative neural networks have revolutionized the approach to creating 3D models. Complex tasks that once took days and required professional skills are now solved automatically and much faster. This makes the process accessible even to those who have never worked with 3D design before.

In 2025, neural networks for creating 3D models are already used in industries such as:

• Game development

  The development of game objects and characters has become much simpler. For instance, studios can now create unique models for NPCs or environmental objects without hiring a team of 3D artists.

• Architecture and interior design

  Architects use neural networks to create building layouts, furniture, and decor. This helps visualize a project before implementation, minimizing errors.

• Cinematography

  In movies and animations, creating 3D models with neural networks saves time and enables the realization of bold ideas. For example, fantastical creatures or complex landscapes can be created faster and more affordably.

• Video blogging

  Similar to cinematography, creating 3D models with neural networks can enhance content for YouTube, TikTok, and Instagram.

The technology is based on machine learning models trained on large datasets containing information about shapes, textures, and the physics of objects. The process includes the following stages:

1. Model training:
   The neural network analyzes millions of 3D objects, studying their geometry, materials, interaction with light, and other objects.
2. Understanding textual or visual requests:
   The user provides a description of an object (e.g., “a car with an aerodynamic design”), and the model interprets it to create geometry.
3. Object generation:
   The neural network builds a 3D model, including textures, lighting, and physical parameters.

This process involves the following software:

• TensorFlow and PyTorch: Core frameworks for developing and training models.
• Blender: Supports plugins and integration with generative neural networks.
• Unity and Unreal Engine: Used to implement ready-made models in games and applications.
• NVIDIA Omniverse: A platform for collaborative work with 3D models and neural networks.

And hardware:

• NVIDIA GPUs (RTX series): Provide powerful computational capabilities for rendering and model training.
• Cloud solutions (Google Cloud, AWS, Azure): Offer remote computing power, making model generation accessible even for those without powerful local hardware.
• AI-specific chips (NVIDIA A100, Google TPU): Enable faster data processing and improved model accuracy.

Popular services and neural networks for 3D image generation

In the previous section, we explored how 3D image generation technology works using neural networks and discussed key industries where it has been applied: from game development to architecture.

Now, let’s move to the practical side and examine popular services that allow you to create 3D models for various applications. These tools have already proven to be reliable and effective solutions for both professionals and beginners.

NVIDIA Edify 3D AI

• 4K Texturing: Supports ultra-high-resolution textures.
• Physical rendering: Takes into account parameters like lighting, materials, and object physics.
• Applications: Creating game characters, complex landscapes, and realistic objects for virtual and augmented reality.

Meshy

• Fast model creation: Generates basic and complex 3D objects in minutes.
• Integration with popular programs: Supports Blender, Unity, and other platforms.
• Flexibility: Allows scaling and adapting objects for various formats.

DreamFusion by Google

• Shape reconstruction: Automatically generates objects with realistic detail.
• Label-free training: No prior data labeling is required for model training.
• Applications: Used for creating prototypes and objects for games, films, and AR/VR.

OpenAI Point-E

• Efficiency: A simple-to-use model that requires minimal computational resources.
• Point cloud generation: The model creates a foundation for further refinement.
• Integration: Compatible with other popular 3D editors.

Luma AI

• Photorealism: High detail in objects.
• Ease of use: Upload a photo, and you get a 3D model.
• Applications: Architecture, virtual tours, creating objects for augmented reality.

Adobe Substance 3D Sampler

• PBR support: Realistic materials compatible with most 3D editors.
• Easy integration: Seamless integration with Adobe Creative Cloud, making it convenient for Photoshop and Illustrator users.
• Applications: Used for creating textures in movies, games, and animations.

Kaedim

• Convenience: Minimal entry barrier—upload an image, get a 3D object.
• Focus on basic models: Suitable for early development stages or simple tasks.
• Applications: Game development, advertising, product prototypes.

Spline AI

• Interactivity: Supports animation and object interaction.
• WebGL: Easy integration for web browsers.
• Applications: Creating web animations and interactive 3D objects.

Daz 3D

• Library of ready-made elements: Thousands of models, textures, and animations.
• Bone animation support: Easy configuration of character movements.
• Applications: Advertising videos, films, conceptual design.

Radiance Fields (NeRF)

• High detail: Enables restoration of textures and colors.
• Applications: VR/AR, cinematography, scientific research.
• Efficiency: Minimal training costs for models.

Learning and professional development in the AI era

With the advancement of neural network technologies and their active integration into 3D modeling and animation, the demand for specialists who can effectively use these tools is growing. Companies are increasingly seeking designers and animators proficient in modern AI solutions, making skills in neural networks a valuable asset for professionals.

• Changing work tools:

  Many processes previously done manually are now automated. To work with technologies like NVIDIA Edify 3D AI, Meshy, or DeepMotion, it’s essential to understand their interfaces, settings, and capabilities.

• Competition in the job market:

  Professionals proficient in AI tools stand out among competitors. Mastering technologies like MidJourney, Stable Diffusion, or DALL-E is becoming an integral part of a successful designer’s or animator’s portfolio.

• Growth of complex projects:

  Large studios and companies demand the implementation of complex tasks within tight deadlines. Neural networks save time and resources, but only if employees know how to use them.

Training in neural networks is tailored for both beginners and experienced professionals. Here are examples of courses:

1. NVIDIA Training Programs
   About the course: Using the NVIDIA Omniverse platform to create collaborative projects, work with 3D models, and textures.
   Outcome: Specialists learn high-tech solutions for 3D art, VR/AR, and games.
2. Blender AI Tools Mastery
   About the course: Integrating neural networks with Blender, automating routine tasks in modeling, texturing, and animation.
   Target audience: Those who already use Blender and want to deepen their knowledge of neural networks.

• Career growth:

  Professionals skilled in neural networks can take higher positions or work as highly paid freelancers.

• Portfolio expansion:

  Using AI allows showcasing a variety of styles and techniques, which attracts clients.

• Participation in innovative projects:

  Companies working with VR/AR, game development, or cinematography actively seek professionals with these skills.

• Global opportunities:

  Freelance platforms like Upwork or Fiverr report increasing demand for AI designers, opening access to international projects.

Conclusion

As we see, the development of neural networks in 2025 has led to significant changes in the field of 3D modeling and animation. These technologies have become not only a powerful automation tool but also opened doors to new creative possibilities.

For professionals and companies looking to stay ahead, learning how to work with neural networks has become a must for success. Platforms and courses provide the necessary knowledge, while the growing demand for specialists experienced in AI opens up new career horizons.

They are not just a technology of the future but a real tool that is already changing the world of creativity, business, and media today.