DiffBMP: Differentiable Rendering with Bitmap Primitives
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908908250988544 |
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| author | Hong, Seongmin Kim, Junghun James Kim, Daehyeop Chung, Insoo Chun, Se Young |
| author_facet | Hong, Seongmin Kim, Junghun James Kim, Daehyeop Chung, Insoo Chun, Se Young |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22625 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DiffBMP: Differentiable Rendering with Bitmap Primitives Hong, Seongmin Kim, Junghun James Kim, Daehyeop Chung, Insoo Chun, Se Young Graphics Computer Vision and Pattern Recognition 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. |
| title | DiffBMP: Differentiable Rendering with Bitmap Primitives |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.22625 |