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arXiv · 2602.22625

DiffBMP: Differentiable Rendering with Bitmap Primitives

Seongmin Hong·Junghun James Kim·Daehyeop Kim·Insoo Chung·Se Young Chun
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Abstract

We introduce DiffBMP, a scalable and efficient differentiable rendering engine for a collection of bitmap images. Our work addresses a limitation that traditional differentiable renderers are constrained to vector graphics, given that most images in the world are bitmaps. Our core contribution is a highly parallelized rendering pipeline, featuring a custom CUDA implementation for calculating gradients. This system can, for example, optimize the position, rotation, scale, color, and opacity of thousands of bitmap primitives all in under 1 min using a consumer GPU. We employ and validate several techniques to facilitate the optimization: soft rasterization via Gaussian blur, structure-aware initialization, noisy canvas, and specialized losses/heuristics for videos or spatially constrained images. We demonstrate DiffBMP is not just an isolated tool, but a practical one designed to integrate into creative workflows. It supports exporting compositions to a native, layered file format, and the entire framework is publicly accessible via an easy-to-hack Python package.

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Seongmin Hong, Junghun James Kim, Daehyeop Kim, Insoo Chung, Se Young Chun. 2026-02-26. DiffBMP: Differentiable Rendering with Bitmap Primitives. https://arxiv.org/abs/2602.22625

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