RenderFlow: Single-Step Neural Rendering via Flow Matching

2026-01-11

arXiv.org

AI Summary

This work proposes the first end-to-end, deterministic, single-step neural rendering framework based on flow matching, addressing the high computational cost of traditional physics-based rendering and the limitations of existing diffusion models—namely, their high latency and stochasticity due to iterative generation, which hinder both physical accuracy and temporal consistency. The method introduces a lightweight, sparse keyframe-guided module to enhance visual fidelity and generalization while naturally supporting inverse rendering tasks. Through an adapter-based fine-tuning strategy, the approach achieves near real-time rendering speeds and significantly outperforms current diffusion-based methods in photorealistic rendering as well as in inverse rendering tasks such as intrinsic image decomposition.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Conventional physically based rendering (PBR) pipelines generate photorealistic images through computationally intensive light transport simulations. Although recent deep learning approaches leverage diffusion model priors with geometry buffers (G-buffers) to produce visually compelling results without explicit scene geometry or light simulation, they remain constrained by two major limitations. First, the iterative nature of the diffusion process introduces substantial latency. Second, the inherent stochasticity of these generative models compromises physical accuracy and temporal consistency. In response to these challenges, we propose a novel, end-to-end, deterministic, single-step neural rendering framework, RenderFlow, built upon a flow matching paradigm. To further strengthen both rendering quality and generalization, we propose an efficient and effective module for sparse keyframe guidance. Our method significantly accelerates the rendering process and, by optionally incorporating sparsely rendered keyframes as guidance, enhances both the physical plausibility and overall visual quality of the output. The resulting pipeline achieves near real-time performance with photorealistic rendering quality, effectively bridging the gap between the efficiency of modern generative models and the precision of traditional physically based rendering. Furthermore, we demonstrate the versatility of our framework by introducing a lightweight, adapter-based module that efficiently repurposes the pretrained forward model for the inverse rendering task of intrinsic decomposition.

Problem

• neural rendering
• diffusion models
• temporal consistency
• physical accuracy
• rendering latency

Innovation

• flow matching
• single-step rendering
• deterministic neural rendering
• sparse keyframe guidance
• inverse rendering