RenderFormer: How neural networks are reshaping 3D rendering

RenderFormer: How neural networks are reshaping 3D rendering

NewsSeptember 10, 2025Artifice Prime

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3D rendering—the process of converting three-dimensional models into two-dimensional images—is a foundational technology in computer graphics, widely used across gaming, film, virtual reality, and architectural visualization. Traditionally, this process has depended on physics-based techniques like ray tracing and rasterization, which simulate light behavior through mathematical formulas and expert-designed models.

Now, thanks to advances in AI, especially neural networks, researchers are beginning to replace these conventional approaches with machine learning (ML). This shift is giving rise to a new field known as neural rendering.

Neural rendering combines deep learning with traditional graphics techniques, allowing models to simulate complex light transport without explicitly modeling physical optics. This approach offers significant advantages: it eliminates the need for handcrafted rules, supports end-to-end training, and can be optimized for specific tasks. Yet, most current neural rendering methods rely on 2D image inputs, lack support for raw 3D geometry and material data, and often require retraining for each new scene—limiting their generalizability.

RenderFormer: Toward a general-purpose neural rendering model

To overcome these limitations, researchers at Microsoft Research have developed RenderFormer, a new neural architecture designed to support full-featured 3D rendering using only ML—no traditional graphics computation required. RenderFormer is the first model to demonstrate that a neural network can learn a complete graphics rendering pipeline, including support for arbitrary 3D scenes and global illumination, without relying on ray tracing or rasterization. This work has been accepted at SIGGRAPH 2025 and is open-sourced on GitHub (opens in new tab).

Architecture overview

As shown in Figure 1, RenderFormer represents the entire 3D scene using triangle tokens—each one encoding spatial position, surface normal, and physical material properties such as diffuse color, specular color, and roughness. Lighting is also modeled as triangle tokens, with emission values indicating intensity.

Figure 1: The figure illustrates the architecture of RenderFormer. It includes a Triangle Mesh Scene with a 3D rabbit model inside a colored cube, a Camera Ray Map grid, a View Independent Transformer (12 layers of Self-Attention and Feed Forward Network), a View Dependent Transformer (6 layers with Cross-Attention and Self-Attention), and a DPT Decoder. Scene attributes—Vertex Normal, Reflectance (Diffuse, Specular, Roughness), Emission, and Position—are embedded into Triangle Tokens via Linear + Norm operations. These tokens and Ray Bundle Tokens (from the Camera Ray Map) are processed by the respective transformers and decoded to produce a rendered image of a glossy rabbit in a colored room. — Figure 1. Architecture of RenderFormer

To describe the viewing direction, the model uses ray bundle tokens derived from a ray map—each pixel in the output image corresponds to one of these rays. To improve computational efficiency, pixels are grouped into rectangular blocks, with all rays in a block processed together.

The model outputs a set of tokens that are decoded into image pixels, completing the rendering process entirely within the neural network.

Dual-branch design for view-independent and view-dependent effects

The RenderFormer architecture is built around two transformers: one for view-independent features and another for view-dependent ones.

The view-independent transformer captures scene information unrelated to viewpoint, such as shadowing and diffuse light transport, using self-attention between triangle tokens.
The view-dependent transformer models effects like visibility, reflections, and specular highlights through cross-attention between triangle and ray bundle tokens.

Additional image-space effects, such as anti-aliasing and screen-space reflections, are handled via self-attention among ray bundle tokens.

To validate the architecture, the team conducted ablation studies and visual analyses, confirming the importance of each component in the rendering pipeline.

Table 1: A table comparing the performance of different network variants in an ablation study. The columns are labeled Variant, PSNR (↑), SSIM (↑), LPIPS (↓), and FLIP (↓). Variants include configurations such as — Table 1. Ablation study analyzing the impact of different components and attention mechanisms on the final performance of the trained network.

To test the capabilities of the view-independent transformer, researchers trained a decoder to produce diffuse-only renderings. The results, shown in Figure 2, demonstrate that the model can accurately simulate shadows and other indirect lighting effects.

Figure 2: The figure displays four 3D-rendered objects showcasing view-independent rendering effects. From left to right: a purple teapot on a green surface, a blue rectangular object on a red surface, an upside-down table casting shadows on a green surface, and a green apple-like object on a blue surface. Each object features diffuse lighting and coarse shadow effects, with distinct highlights and shadows produced by directional light sources. — Figure 2. View-independent rendering effects decoded directly from the view-independent transformer, including diffuse lighting and coarse shadow effects.

The view-dependent transformer was evaluated through attention visualizations. For example, in Figure 3, the attention map reveals a pixel on a teapot attending to its surface triangle and to a nearby wall—capturing the effect of specular reflection. These visualizations also show how material changes influence the sharpness and intensity of reflections.

Figure 3: The figure contains six panels arranged in two rows and three columns. The top row displays a teapot in a room with red and green walls under three different roughness values: 0.3, 0.7, and 0.99 (left to right). The bottom row shows the corresponding attention outputs for each roughness setting, featuring the teapot silhouette against a dark background with distinct light patterns that vary with roughness. — Figure 3. Visualization of attention outputs

Training methodology and dataset design

RenderFormer was trained using the Objaverse dataset, a collection of more than 800,000 annotated 3D objects that is designed to advance research in 3D modeling, computer vision, and related fields. The researchers designed four scene templates, populating each with 1–3 randomly selected objects and materials. Scenes were rendered in high dynamic range (HDR) using Blender’s Cycles renderer, under varied lighting conditions and camera angles.

The base model, consisting of 205 million parameters, was trained in two phases using the AdamW optimizer:

500,000 steps at 256×256 resolution with up to 1,536 triangles
100,000 steps at 512×512 resolution with up to 4,096 triangles

The model supports arbitrary triangle-based input and generalizes well to complex real-world scenes. As shown in Figure 4, it accurately reproduces shadows, diffuse shading, and specular highlights.

Figure 4: The figure presents a 3×3 grid of diverse 3D scenes rendered by RenderFormer. In the top row, the first scene shows a room with red, green, and white walls containing two rectangular prisms; the second features a metallic tree-like structure in a blue-walled room with a reflective floor; and the third depicts a red animal figure, a black abstract shape, and a multi-faceted sphere in a purple container on a yellow surface. The middle row includes three constant width bodies (black, red, and blue) floating above a colorful checkered floor; a green shader ball with a square cavity inside a gray-walled room; and crystal-like structures in green, purple, and red on a reflective surface. The bottom row showcases a low-poly fox near a pink tree emitting particles on grassy terrain; a golden horse statue beside a heart-shaped object split into red and grey halves on a reflective surface; and a wicker basket, a banana and a bottle placed on a white platform. — Figure 4. Rendered results of different 3D scenes generated by RenderFormer

RenderFormer can also generate continuous video by rendering individual frames, thanks to its ability to model viewpoint changes and scene dynamics.

3D animation sequence rendered by RenderFormer

Looking ahead: Opportunities and challenges

RenderFormer represents a significant step forward for neural rendering. It demonstrates that deep learning can replicate and potentially replace the traditional rendering pipeline, supporting arbitrary 3D inputs and realistic global illumination—all without any hand-coded graphics computations.

However, key challenges remain. Scaling to larger and more complex scenes with intricate geometry, advanced materials, and diverse lighting conditions will require further research. Still, the transformer-based architecture provides a solid foundation for future integration with broader AI systems, including video generation, image synthesis, robotics, and embodied AI.

Researchers hope that RenderFormer will serve as a building block for future breakthroughs in both graphics and AI, opening new possibilities for visual computing and intelligent environments.

The post RenderFormer: How neural networks are reshaping 3D rendering appeared first on Microsoft Research.

Origianl Creator: Yue Dong
Original Link: https://www.microsoft.com/en-us/research/blog/renderformer-how-neural-networks-are-reshaping-3d-rendering/
Originally Posted: Wed, 10 Sep 2025 16:00:00 +0000

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