Voxify3D：像素艺术遇上体渲染
Voxify3D: Pixel Art Meets Volumetric Rendering
提出正交像素艺术监督与可微 Gumbel-Softmax 量化，将 3D 网格优化与 2D 像素艺术监督桥接，为离散颜色空间下的可微优化和风格化体素生成提供可复用技术。
原文依据 orthographic pixel art supervision that eliminates perspective distortion for precise voxel-pixel alignment
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论体素艺术是一种在游戏和数字媒体中广泛使用的独特风格化形式，然而从 3D 网格自动生成体素艺术仍然具有挑战性，因为其在几何抽象、语义保持和离散颜色一致性方面存在相互冲突的需求。现有方法要么过度简化几何形状，要么无法实现体素艺术那种像素级精确、调色板受限的美学效果。我们提出了 Voxify3D，一个可微的两阶段框架，将 3D 网格优化与 2D 像素艺术监督相结合。我们的核心创新在于三个组件的协同集成：（1）正交像素艺术监督，消除透视畸变以实现精确的体素-像素对齐；（2）基于块的 CLIP 对齐，在离散化级别上保持语义；（3）调色板约束的 Gumbel-Softmax 量化，支持在离散颜色空间上进行可微优化，并具有可控的调色板策略。这种集成解决了根本性挑战：极端离散化下的语义保持、通过体渲染实现像素艺术美学，以及端到端的离散优化。实验表明，该方法在不同角色和可控抽象程度（2-8 种颜色，20x-50x 分辨率）下均表现出优越性能（37.12 CLIP-IQA，77.90% 用户偏好）。项目页面：https://yichuanh.github.io/Voxify-3D/
英文原文摘要 1343 字符
Voxel art is a distinctive stylization widely used in games and digital media, yet automated generation from 3D meshes remains challenging due to conflicting requirements of geometric abstraction, semantic preservation, and discrete color coherence. Existing methods either over-simplify geometry or fail to achieve the pixel-precise, palette-constrained aesthetics of voxel art. We introduce Voxify3D, a differentiable two-stage framework bridging 3D mesh optimization with 2D pixel art supervision. Our core innovation lies in the synergistic integration of three components: (1) orthographic pixel art supervision that eliminates perspective distortion for precise voxel-pixel alignment; (2) patch-based CLIP alignment that preserves semantics across discretization levels; (3) palette-constrained Gumbel-Softmax quantization enabling differentiable optimization over discrete color spaces with controllable palette strategies. This integration addresses fundamental challenges: semantic preservation under extreme discretization, pixel-art aesthetics through volumetric rendering, and end-to-end discrete optimization. Experiments show superior performance (37.12 CLIP-IQA, 77.90% user preference) across diverse characters and controllable abstraction (2-8 colors, 20x-50x resolutions). Project page: https://yichuanh.github.io/Voxify-3D/