How AI Will Change 3D Animation – On Pipeline Replacement

Current State of AI-Based 3D Animation Production Tools

AI has only begun to be used in earnest in animation production in recent years. The growth rate becomes even more tangible when verified by numbers. As of 2024, the generative AI market in the animation sector is valued at approximately $2.1 billion, and is projected to grow to $15.9 billion by 2030. According to separate research organizations, the compound annual growth rate (CAGR) reaches as high as 39.8%. During the same period, the overall 3D animation market is expected to grow from $25.2 billion in 2024 to $51 billion by 2030, nearly doubling.

At the center of this growth are technologies such as machine learning, deep learning, GAN (Generative Adversarial Networks), and transformer models. Simply put, it’s technology where computers learn vast amounts of animation data and then create drawings and movements on their own. A prime example is tools like Krikey AI, which automatically generates motion sequences where characters move realistically with just a single line of text description. AI can create background environments, textures (surface quality and patterns), and lip-sync matching dialogue with minimal input. Analysis shows that AI can automate 60-70% of the entire animation production time.

Key Stages and Limitations of Traditional 3D Animation Pipeline

Creating a complete 3D animation involves going through three major stages. This process is called a ‘pipeline,’ referring to a workflow where each stage is connected sequentially, like a factory production line. Breaking it down by stage:

1. Pre-Production: Concept development, script writing, storyboard creation (a blueprint that lists scenes as drawings), and character model sheet creation. If this stage is done poorly, budget overruns and chaos will occur in all subsequent processes.
2. Production: 3D Modeling (creating characters and backgrounds in 3D), rigging (embedding a skeleton in the character to make it moveable), and animating (implementing actual movements). This is the stage that requires the most personnel and time.
3. Post-Production: Compositing (combining multiple video layers), sound design, and editing are performed to complete the final result.

The problem is that this pipeline is strictly sequential. If an error occurs in an earlier stage, its ripple effects disrupt the entire subsequent process. Working without a clear pipeline results in missed deadlines, broken communication between teams, and quality degradation all at once. The animated film “The Thief and the Cobbler” is actually recorded as an extreme case, where it was left unfinished for decades due to repeated unplanned revisions. Thus, the traditional pipeline revealed its limitations in both speed and flexibility due to its inherent structural rigidity.

AI Replacing Modeling, Rigging, and Rendering Work

The area where AI has most quickly and forcefully entered is the repetitive work of the production stage. Let’s examine specifically which tasks have changed.

• ▪ Automatic In-betweening: AI automatically generates in-between frames between keyframes (key action scenes). Traditionally, this work consumed the most personnel and time, but with AI adoption, the most grueling repetitive tasks of junior animators have been greatly reduced.
• ▪ AI Character Rigging: Technology that automatically sets up skeletal structure for characters. Rigging that once required skilled technicians to work for days can now be completed within hours. It has evolved to the point where the basic skeleton is set up with just one AI rigging auto-generation button within the work tool.
• ▪ Text-to-Animation: Creates character movements from text descriptions alone. For example, if you input “character waves hand and runs over,” AI outputs an animation sequence implementing that movement.
• ▪ Neural Rendering: Technology that dramatically increases high-quality rendering (final image output) speed using algorithms that mimic human neural networks. It has currently reached the implementation stage in actual movie, game, and advertising projects, with algorithms that predict and correct lighting and texture in real-time already being adopted by studios.
• ▪ Automatic Background Generation and Coloring: The Nagoya-based KK Design studio achieved remarkable results by applying a customized Stable Diffusion model to background art and coloring, reducing background art work that previously took one week to just 5 minutes. They also reduced production time for 5-second animation clips from one week to one day.

Changes in Production Costs and Personnel Structure from AI Adoption

The reason AI adoption is received most sensitively in practice is due to changes in costs and personnel structure. Taking the Japanese animation industry as an example, 38% of workers earn less than 200,000 yen per month (approximately 1.35 million won in Korean currency), and the average monthly working hours are 219 hours, significantly exceeding the average of 168 hours for general workers. AI is receiving attention as a practical tool to address this structural problem.

Animon.ai, released in 2025, states it can reduce work that previously took 72 hours to just 3 minutes. This productivity improvement is leading toward personnel redeployment rather than workforce reduction. KK Design studio has clearly formalized the purpose of AI adoption as ‘improving work environment, not workforce reduction.’ Work like douga (in-between frames) is currently often outsourced to studios in Southeast Asia, but if AI replaces this work, both the outsourcing dependency and quality imbalance problems are solved simultaneously.

The role of animators is also changing. There is a shift from ‘technical executor’ to ‘creative director,’ and emotional expression, storytelling judgment, and artistic vision are evaluated as uniquely human domains that AI cannot replace. Current production workflows are diverging into three paths: traditional methods, AI-utilized methods, and hybrid methods combining both.

Future Changes and Challenges in 3D Animation Pipeline

What will the 3D animation pipeline look like in the future? The most notable change is that the pipeline itself no longer needs to be sequential. As AI handles intermediate stages like rigging, background generation, and rendering in real-time, production teams can focus on parts requiring creativity like storytelling and character performance. The convergence of AI, machine learning, and CGI is changing not just the efficiency but the very expression methods of animation.

Advertising agencies are moving toward producing dynamic brand content that automatically transforms according to market demand through AI, and game companies are transitioning to large-scale character and environment production through AI-based asset pipelines. The global animation industry is projected to reach $587.3 billion by 2026, and industry experts agree that AI will grow this market even more rapidly.

However, challenges are clear. Here are the core challenges the industry currently faces:

• ▪ Skills Retraining Issues: Art directors and animators cannot rely solely on existing workflow knowledge. Acquiring new competency in handling AI tools has become essential.
• ▪ Maintaining Creative Quality: Without setting standards on how far to apply AI, there’s a risk that results become uniform. Decision-makers must design the scope of AI adoption in ways that don’t compromise creative quality.
• ▪ Growth Path for Junior Personnel: Repetitive work like douga is also a training process through which junior animators learn their craft. If AI replaces this work, the growth ladder for future-generation animators may disappear—a concern that has been raised.
• ▪ Copyright and Training Data Issues: Copyright issues regarding existing works used in AI training have not yet been clarified by international standards.

Ultimately, AI’s role in the 3D animation pipeline is not to eliminate human animators but to help humans focus on more creative work. It functions in the direction of expanding the very scope of production possibilities, and this trend will accelerate further in the future.