Diffusion Renderer by Nvidia: Neural Inverse and Forward Rendering with Video Diffusion Models

Diffusion Renderer: Estimating Geometry, Depth, and Material Properties from Video

In this article, I’ll walk you through an incredible tool called Diffusion Renderer by Nvidia. This tool has the ability to take a video and estimate the geometry, depth, material properties, and other features of objects within it.

What Does Diffusion Renderer Do?

Estimating Geometry and Depth

The Diffusion Renderer can analyze a video and calculate the depth of everything in it. This means it can determine how far objects are from the camera, which is crucial for creating realistic 3D representations. Additionally, it calculates the normal, which defines the surfaces of 3D objects in the video.

Diffusion Renderer Home

This is particularly important for simulating realistic lighting and shading.

Calculating Material Properties

Beyond geometry, the tool also estimates the albedo, which is the base color of an object without any shading or lighting applied.

It also determines the metallic properties of objects, essentially measuring how reflective they are. Lastly, it estimates the roughness of objects, which affects how light interacts with their surfaces.

Diffusion Renderer Model Overview:

Feature	Details
Model Name	Diffusion Renderer
Functionality	Neural inverse and forward rendering using video diffusion models
Paper	Diffusion Renderer Paper
arXiv	arxiv:2501.18590
Demo Video	Watch Demo
Key Components	Inverse Rendering, Forward Rendering
Main Features	Geometry estimation, Depth estimation, Material property calculation
Applications	Scene understanding, Video manipulation, Enhanced rendering capabilities

Examples of Diffusion Renderer in Action

Input Video Analysis

Let’s look at some examples to see how this works. In the top-left corner of the examples provided, you’ll see the input video. The Diffusion Renderer can estimate all the properties mentioned above directly from this video. Even in complex scenes with multiple objects, the tool performs exceptionally well.

Manipulating Video Properties

Because the Diffusion Renderer can understand and estimate these properties, it allows for some incredible manipulations. For instance, you can change the color, lighting, or reflectiveness of objects in the video. Here are some examples of this in action:

Relighting: The input video is on the left, but you can adjust the lighting however you want. Notice how the lighting and shadows differ in each of the four videos. Compared to existing relighting methods, this tool is far more accurate and consistent.
Changing Roughness and Reflectiveness: In the top row, you can see a ball and a horse changing in terms of roughness and reflectiveness. The same applies to the objects in the bottom row.

Inserting Objects into Videos

Another fascinating feature is the ability to insert objects into a video while ensuring they align with the existing lighting. For example:

Inserting a Sync object into the scene makes it look natural and well-integrated.
Adding a table to the scene also demonstrates how seamlessly the tool blends new objects into the video.

Diffusion Renderer Object Insertion

How Does Diffusion Renderer Work?

The Process

The Diffusion Renderer works in two main stages: inverse rendering and forward rendering.

Inverse Rendering Stage:
- The tool takes an input video and runs it through a diffusion model.
- It estimates the color, depth, and other properties of the objects in the video, one attribute at a time.
Forward Rendering Stage:
- The estimates from the inverse rendering stage are used to generate new frames under different lighting conditions or with modified properties.
- The final output is a video with the desired changes applied.

Key Advantages

One of the most impressive aspects of this AI is that it doesn’t require any explicit 3D or lighting data. Unlike traditional methods, it can estimate and edit all of this information using just an input video.

Motivation Behind Diffusion Renderer

Overcoming Limitations of Classic PBR

The Diffusion Renderer was developed to address the limitations of classic physically-based rendering (PBR) methods. Traditional PBR relies on explicit 3D geometry, such as meshes. When this geometry isn’t available, screen space ray tracing (SSRT) struggles to accurately represent shadows and reflections.

Forward Rendering Without Explicit Geometry

The forward renderer in the Diffusion Renderer synthesizes photorealistic lighting effects without needing explicit path tracing or 3D geometry. It’s also designed to tolerate noisy buffers, which is a common issue with state-of-the-art inverse rendering models.

Forward Rendering in Detail

Video Generation from G-Buffers

The forward renderer generates accurate shadows and reflections that remain consistent across different viewpoints. These lighting effects are synthesized entirely from an environment map, even though the input G-buffers contain no explicit shadow or reflection information.

Qualitative Comparison

When compared to neural baselines, the Diffusion Renderer produces higher-quality inter-reflections and shadows. This results in more accurate and realistic outputs.

Inverse Rendering in Detail

General-Purpose Solution

The inverse renderer provides a general-purpose solution for de-lighting. It produces accurate and temporally consistent scene attributes, such as:

Normals
Albedo
Roughness
Metallicity

Relighting with Diffusion Renderer

Effectiveness in Relighting Tasks

The combined inverse and forward rendering model is highly effective in relighting tasks. By using the estimated G-buffers from the inverse renderer, the tool can relight scenes under different lighting conditions. This demonstrates its versatility and precision in handling complex visual tasks.

Conclusion

The Diffusion Renderer by Nvidia is a powerful tool that opens up new possibilities for video analysis and manipulation. By estimating geometry, depth, and material properties from videos, it allows for realistic relighting, object insertion, and property adjustments. Its ability to work without explicit 3D or lighting data sets it apart from traditional methods.