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Modular Primitives for High-Performance Differentiable Rendering

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arxiv 2011.03277 v1 pith:HHZI7IPI submitted 2020-11-06 cs.GR cs.CVcs.LG

Samuli Laine , Janne Hellsten , Tero Karras , Yeongho Seol , Jaakko Lehtinen , Timo Aila This is my paper
classification cs.GRcs.CVcs.LG
keywords graphicsmodulardesigndifferentiablehigh-performanceperformancepipelinesprimitives
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We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DiffGI: Differentiable Geometry Images for High-Fidelity Thin-Shell 3D Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    DiffGI replaces binary occupancy maps in geometry images with a continuous 2D TSDF and a differentiable Marching Squares extractor, enabling end-to-end training of a compact latent diffusion model for thin-shell 3D ge...

  2. MRD: Using Physically Based Differentiable Rendering to Probe Vision Models for 3D Scene Understanding

    cs.CV 2025-12 conditional novelty 6.0 of 10

    MRD finds physically different 3D scenes that reproduce a target model activation, revealing which shape and material properties vision models are sensitive to.

  3. UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks

    cs.CR 2025-10 conditional novelty 6.0 of 10

    UnDREAM enables optimization of adversarial textures on arbitrary 3D objects inside Unreal Engine by bridging the simulator to the differentiable renderer Mitsuba.

  4. SAT: Supervisor Regularization and Animation Augmentation for Two-process Monocular Texture 3D Human Reconstruction

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A two-stage Gaussian-splatting framework with supervisor feature regularization and online animation augmentation improves monocular textured 3D human reconstruction on CustomHuman and THuman3.0.

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