Multimodal Generation Technology Panorama: From "Visual Toy" to "Physical World Simulator"

Preface:
For a long time, Multimodal AI was viewed as an "amusing toy." It could generate beautiful anime illustrations or synthesize a funny video of Trump dancing, but when you tried to use it to make a continuous animation of even 3 minutes, or design a 3D asset importable to Unity, it exposed fatal flaws: character flickering, physics collapse, style drift.

In March 2025, with the concentrated explosion of Sora v2 (hypothetical version), Runway Gen-4, and Midjourney 3D, the critical point was breached. Multimodal AI is completing the evolution from "Generating Pixels" to "Simulating Physics." This article delves into the technological driving forces and industrial echoes behind this revolution.

Chapter 1: The Revolution of Controllability in Visual Generation

The biggest enemy of Generative AI is not "drawing poorly," but "drawing too randomly." In industrial processes, Controllability overrides everything.

1.1 The Ultimate Solution for IP Consistency

In 2024, to make AI consistently draw the same character, the community invented various "patches" like IP-Adapter and FaceID.
In 2025, ReferenceNet architecture became a standard feature of mainstream models.

1.1.1 What is ReferenceNet?

It is an encoder parallel to the main generation network.

Workflow: You input a "Character Design Sheet." ReferenceNet extracts high-dimensional features of the image (not just the face, but also clothing textures, hair accessories details).
Injection Mechanism: These features are precisely injected into every layer of the generation network via Cross-Attention layers.
Result: No matter how you change the Prompt (e.g., "running in the rain," "eating ramen"), the generated person remains the same person, down to the button details on their clothes.

1.2 Native Support for Composition and Layers

Adobe Firefly 3.0 taught the industry a lesson: Layers are the soul of design.
Current multimodal models no longer output a flat JPG, but can directly output PSD formats.

Alpha Channel Prediction: The model has learned to distinguish between "foreground" and "background."
Vector Output: For Logo and icon design, the quality of SVG generation has reached commercial levels, thoroughly solving the problem of bitmap enlargement blur.

Chapter 2: Video Generation: Searching for the Holy Grail of "World Models"

OpenAI once said: "Sora is not just a video generator; it is a World Simulator." This statement began to reveal its true meaning in 2025.

2.1 From "Moving Pictures" to "Physics Simulation"

Early video generation (like Pika 1.0) was essentially Image Animation.
Current Video Native Models begin to understand physical laws.

2.1.1 Case: Liquid and Gravity

Old Model: Generating "a glass of water spilling," the water might float in the air like jelly or vanish into thin air.
New Model: Water flows down the table edge, splashing droplets follow parabolic motion, and the water surface shows correct light refraction.
Technical Principle: The model unsupervisedly learned implicit expressions of $F=ma$ (Newton's Second Law) and fluid dynamics from massive video data. It isn't calculating physics formulas, but its predictions conform to physics formulas.

2.2 Breakthroughs in Duration and Coherence

Context Fragmentation is the reason for collapse as video gets longer.
Application of Ring Attention in Video: Similar to LLMs, long video generation also introduced Ring Attention. This allows AI to generate continuous shots up to 5 minutes long, with character attire remaining consistent from start to finish.

Chapter 3: 3D Generation: The Last Mile of Industrialization

The production cost of 3D assets is extremely high. Modeling, texturing, and rigging a 3A game character often takes a senior artist weeks. AI is compressing this process to minutes.

3.1 The Explosion of Gaussian Splatting

Although NeRF (Neural Radiance Fields) has good effects, rendering is too slow for game engines.
3D Gaussian Splatting (3DGS) completely changed the game in 2025.

Principle: Representing scenes using thousands of "ellipsoids" (Gaussian spheres) with color, transparency, and direction.
Advantages:
1. Real-time Rendering: Can run at 60fps even on mobile phones.
2. Generation Speed: Generating a high-quality 3DGS scene from a video or a few photos takes only a few seconds.

3.2 Topology Optimization and Auto-Rigging

Generated 3D models are usually messy Meshes, unusable for animation.
The AutoRetopo v4 model released this week solved this:

Quad Retopology: Automatically converting messy triangular faces into quad faces (Quads) that meet wiring standards.
Auto-Rigging: AI identifies that this is a "bipedal humanoid," automatically generates a skeleton inside this Mesh, and paints skin weights.
This means: Generated 3D models can be directly imported into Maya or Unity for animation.

Chapter 4: Industrial Reconstruction: Earthquakes in Hollywood and Gaming

Technological change inevitably triggers change in production relations.

4.1 Film: Previs is the Final Cut

In the past, directors drew storyboards and made rough 3D Previs.
Now, AI-generated dynamic storyboards (Animatic) are of such high quality that they can even be directly used as part of the final cut (e.g., backgrounds, crowd extras).

Tyler Perry Pauses Studio Expansion: This is a landmark event. When green screen backgrounds can be perfectly generated by AI, the demand for physical set construction plummets.

4.2 Gaming: The Explosion of UGC

When the threshold for generating 3D assets drops to "speaking a sentence," the gaming industry will welcome the golden age of UGC (User Generated Content).

Evolution of Roblox: Players no longer build houses with blocks, but tell AI "build me a Gothic castle," and AI instantly generates the model and places it in the game.

Chapter 5: The Dark Side: Deepfake and Trust Crisis

We cannot just sing praises. The rapid development of multimodal technology has also opened Pandora's box.

5.1 The Darkest Hour of Distinguishing Real from Fake

In 2025, distinguishing AI video with the naked eye is impossible. Biometric identification (like iris scanning, voice print locks) faces huge challenges.

Injection Attacks: Hackers generate a video stream containing the victim's voice print and facial features via AI, injecting it directly into the camera data channel to fool bank facial recognition.

5.2 The Battle of Spear and Shield

Adversarial Sample Watermarking: A technology to protect personal photos. Adding invisible noise to your selfies so that when AI tries to train LoRA with this image, the generated image collapses completely.
Mandatory C2PA Standard: New cameras released by Sony and Canon have cryptographic signatures stamped on photos at the hardware level. News agencies will refuse to adopt photos without this signature.

Conclusion: Simulator of the Physical World

The ultimate goal of Multimodal AI is not drawing, but understanding the physical world.
When we have an AI model that can perfectly simulate light and shadow, gravity, fluids, and even biological behavior, it is no longer just a content generation tool, but a General Physical World Simulator.
It can be used to train self-driving cars, simulate robot grasping, and even deduce climate change.
This is the sea of stars for multimodal generation.

This document is written by the Augmunt Institute for Frontier Technology, focusing on frontier progress in multimodal technology in Q1 2025.