DANTE-W: Diffuse Albedo Neural Texturing in the Wild

Classical mesh texturing techniques blend captured multi-view images directly, which inevitably suffer from baked-in shading andcasted shadows that compromise visual fidelity during relighting. To cir-cumvent this issue, we present a neural texturing framework, namelyDante-w, to enable high-fidelity diffuse albedo texture recovery fromunstructured image collections for large-scale, in-the-wild scenes, whichintegrates seamlessly with traditional 3D reconstruction pipelines. Givena reconstructed mesh and its surface parameterization, our method fusesview-space generative albedo priors into a coherent texture space viaan expressive neural representation, while substantially enhancing fine-grained textural details through physically principled neural rendering.To comprehensively evaluate our method, we curate a benchmark datasetfeaturing diverse, fine-grained textures, comprising both real-world in-the-wild scenes and synthetic objects. Extensive experiments verify theeffectiveness of our approach in reconstructing accurate albedo texturesand boosting relighting fidelity. Project page: dante-wild.github.io.