mlsanityData to 7 Oct 2026

RaPD: Resolution-Agnostic Pixel Diffusion via Semantics-Enriched Implicit Representations

arXiv 2605.15908 ↗First posted 15 May 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Natural images are continuous, yet most generative models synthesize them on discrete grids, limiting resolution-flexible generation. Continuous neural fields enable resolution-free rendering, but prior methods introduce continuity only at the decoding stage as an interpolation module, leaving the generative latent space discretized and reconstruction-oriented. We propose RaPD (Resolution-agnostic Pixel Diffusion), which performs diffusion in a continuous Neural Image Field (NIF) latent space. RaPD bridges this reconstruction-generation gap with Semantic Representation Guidance for generation-aware latent learning and a Coordinate-Queried Attention Renderer for coordinate-conditioned, scale-aware rendering. A single denoised latent can be rendered at arbitrary resolutions by changing only the query coordinates, keeping diffusion cost fixed. Experiments demonstrate superior generation quality and resolution scalability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 69 of its 81 references that are in the map. First posted on arXiv 15 May 2026.

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References · 69

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38 more
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