The Illusion of Four LEDs Becoming a Snake: Why the Picture Appears to Move

When four LEDs are mounted on a square frame and illuminated electronically in sequence, no one sees "four lights." Instead, they see "a snake creeping along the edges of the square." In reality, no object is actually moving. This happens because the brain binds the information of stationary light arriving at different times into a single continuous motion, creating an illusion. This phenomenon is called 'Beta movement'¹.

Animation is a technology that leverages this illusion of the brain. Whether drawing by hand, moving 3D models with a computer, or instructing an AI to generate video, the fundamental principle remains the same. It involves displaying static images rapidly in succession, causing the brain to interpret them as movement¹. The only thing that has changed is the 'method' by which the static frames are produced². We will examine how these technologies—from the principles of how the brain perceives motion to traditional cel animation, 3D rigging, and recent AI video generation—apply the same principle in different ways.

The Illusion of Motion Created by the Brain

This perceptual characteristic functions not only in laboratory LED setups but also in LED displays encountered in everyday life¹.

The basic unit traditional animation uses to implement this illusion is the 'frame.' Each individual drawing, flipping by rapidly, stimulates the brain's visual system, transforming a static array into dynamic perception¹. Ultimately, creating animation is akin to meticulously assembling static images so that the viewer's brain completes the motion.

The Structure of Cel Animation: Making Hand-Drawn Images Come Alive

In the past, studios produced transparent plastic sheets, known as 'cells,' to induce this illusion in the brain. The term cel animation derives from the first part of the word Celluloid; it is a classic 2D technique that creates movement by drawing frames on thin, transparent plastic³.

The workflow resembles a meticulous assembly process. First, the background is drawn separately in an opaque state, while characters have their outlines traced in black ink on transparent cells. Then, the sheets are flipped over, and colors are applied from the back to complete the coloring³. The completed cells are placed over the background and photographed one frame at a time to create video³. Since character cells could be swapped and placed over the same background, backgrounds were often depicted with greater detail than the characters themselves³.

The frame rate used during this process is typically between 12 and 24 frames per second³. With the introduction of digital tools, the use of physical cell sheets has decreased, but the underlying physical principle of creating static images for the brain to perceive has not changed³.

The Language of Animation: Creating a Sense of Physical Weight

If images are simply flipped at a rate of 12 per second, the brain may only perceive them as "a rapid change in screen." For movement to feel natural, mechanisms beyond simple positional changes are required to convey a sense of physical weight and inertia.

Disney animators Ollie Johnston and Frank Thomas developed these techniques as principles of animation⁴. These principles have become the baseline for designing motion not only in hand-drawn animation but also in current 3D workflows⁴.

For example, 'Squash and Stretch' imparts a sense of weight through object deformation. A ball flattens when it hits the ground and stretches when it bounces up⁴. The principle of 'Anticipation' creates realism through pre-action warm-up movements. A character might slightly crouch before jumping, allowing the audience to anticipate the upcoming motion⁴. These devices make the spaces between frames appear not as simple positional changes, but as if actual objects are experiencing impact and resisting gravity⁴.

The 3D Animation Pipeline: Implanting Bones and Computing Motion

In computer-based 3D animation, outlines are not manually drawn for each frame. Instead, motion is created through a 'pipeline' where people, hardware, and software work in a set sequence to produce the final output².

The core of this process is 'Rigging.' Rigging is the task of implanting a skeletal structure inside a 3D model, enabling animators to move complex geometric shapes quickly and efficiently by part². The animation stage, where facial expressions and actions are applied to rigged characters, is the most time-consuming and critical task in the entire pipeline².

Once the motion is defined, the 'Rendering' stage follows. Rendering is a computational process that converts 3D data into final 2D images or video layers². In 3D animation, scenes are rendered in separate layers—including objects, colors, backgrounds, foregrounds, shadows, and highlights—which are then combined into a single frame during the post-production compositing stage². Effectively, the method of creating the static screens delivered to the brain has shifted from physical photography to computer computation.

AI Video Generation: Bypassing Human Hands

Recent AI video generation technologies are changing the entity that creates the frames itself. In October 2025, Developer Kim Hyun-ho of KBDS, an IT subsidiary of KB Financial Group, shared an experiment using Google DeepMind’s AI video generation model 'Veo3'⁵.

Developer Kim Hyun-ho first converted a static image of the KB Financial Group mascot into a 'seaside background' using NanoBanana, then input it into Veo3. He added a short text instruction (prompt) describing a 'scene where the character waves lightly in greeting'⁵. Without 3D modeling, rigging, or hand-drawing, the static character generated a video where it actually waved its hand, complete with background and music⁵.

However, this method has limitations that require verification. According to Developer Kim Hyun-ho’s observations, the generated video exhibited phenomena where the shape of specific characters subtly deformed from frame to frame, or where facial consistency was reduced⁵. While AI strives to maintain consistency between frames, this case demonstrates that human refinement and adjustment remain necessary for full vitality⁵.

Just as the brain sees a snake when four LEDs light up, the fact that the viewer’s brain creates motion remains the same whether flipping cells, moving skeletons, or entering prompts¹. Understanding that life is not entered into drawings, but rather that the brain weaves a series of stills into motion, clarifies the principle that runs through the past and future of animation.