WIPES: Wavelet-based Visual Primitives
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915451121958912 |
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| author | Zhang, Wenhao Zhu, Hao Wu, Delong Kang, Di Bao, Linchao Cao, Xun Ma, Zhan |
| author_facet | Zhang, Wenhao Zhu, Hao Wu, Delong Kang, Di Bao, Linchao Cao, Xun Ma, Zhan |
| contents | Pursuing a continuous visual representation that offers flexible frequency modulation and fast rendering speed has recently garnered increasing attention in the fields of 3D vision and graphics. However, existing representations often rely on frequency guidance or complex neural network decoding, leading to spectrum loss or slow rendering. To address these limitations, we propose WIPES, a universal Wavelet-based vIsual PrimitivES for representing multi-dimensional visual signals. Building on the spatial-frequency localization advantages of wavelets, WIPES effectively captures both the low-frequency "forest" and the high-frequency "trees." Additionally, we develop a wavelet-based differentiable rasterizer to achieve fast visual rendering. Experimental results on various visual tasks, including 2D image representation, 5D static and 6D dynamic novel view synthesis, demonstrate that WIPES, as a visual primitive, offers higher rendering quality and faster inference than INR-based methods, and outperforms Gaussian-based representations in rendering quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12615 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WIPES: Wavelet-based Visual Primitives Zhang, Wenhao Zhu, Hao Wu, Delong Kang, Di Bao, Linchao Cao, Xun Ma, Zhan Computer Vision and Pattern Recognition Pursuing a continuous visual representation that offers flexible frequency modulation and fast rendering speed has recently garnered increasing attention in the fields of 3D vision and graphics. However, existing representations often rely on frequency guidance or complex neural network decoding, leading to spectrum loss or slow rendering. To address these limitations, we propose WIPES, a universal Wavelet-based vIsual PrimitivES for representing multi-dimensional visual signals. Building on the spatial-frequency localization advantages of wavelets, WIPES effectively captures both the low-frequency "forest" and the high-frequency "trees." Additionally, we develop a wavelet-based differentiable rasterizer to achieve fast visual rendering. Experimental results on various visual tasks, including 2D image representation, 5D static and 6D dynamic novel view synthesis, demonstrate that WIPES, as a visual primitive, offers higher rendering quality and faster inference than INR-based methods, and outperforms Gaussian-based representations in rendering quality. |
| title | WIPES: Wavelet-based Visual Primitives |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.12615 |