RenderFormer

Transformer-based Neural Rendering of Triangle Meshes with Global Illumination

SIGGRAPH 2025
1State Key Lab of CAD and CG, Zhejiang University
2Microsoft Research Asia
3College of William & Mary
RenderFormer results

Introduction

We present RenderFormer, a neural rendering pipeline that directly renders an image from a triangle-based representation of a scene with full global illumination effects and that does not require per-scene training or fine-tuning.

Mesh to Image, End to End

Instead of taking a physics-centric approach to rendering, we formulate rendering as a sequence-to-sequence transformation where a sequence of tokens representing triangles with reflectance properties is converted to a sequence of output tokens representing small patches of pixels.

Simple Transformer Architecture with Minimal Prior Constraints

RenderFormer follows a two stage pipeline: a view-independent stage that models triangle-to-triangle light transport, and a view-dependent stage that transforms a token representing a bundle of rays to the corresponding pixel values guided by the triangle-sequence from the the view-independent stage. Both stages are based on the transformer architecture and are learned with minimal prior constraints. No rasterization, no ray tracing.

Model architecture

Videos

Check out extra video results including uncompressed videos and some reference videos.

Teaser Scenes

Dynamic demonstrations of RenderFormer's capabilities, showing object rotations, lighting changes, and material adjustments.

Cornell Box Roughness Adjustment
Bunny Roughness Adjustment
Tree Light Change
Tree Object Rotation
Fancy Scene Rotation
Composed Scene View Change

Animations

RenderFormer can render animations of scenes.

Cascade Cube Animation
Animated Crab
Source: Bohdan Lvov
Gyroscope Motion
Animated Character
Source: mortaleiros
Marching Cubes Animation
Robot Animation
Source: Gouhadouken

Physical-Based Simulations

RenderFormer can render physically simulated scenes with complex dynamics and interactions.

Bowling Ball Physics Simulation
Source: SINOFWRATH
Rotating Box Dynamics
Constant Width Body Simulation

BibTeX

@inproceedings {zeng2025renderformer,
    title      = {RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination},
    author     = {Chong Zeng and Yue Dong and Pieter Peers and Hongzhi Wu and Xin Tong},
    booktitle  = {ACM SIGGRAPH 2025 Conference Papers},
    year       = {2025}
}