发表机构
Guangdong Provincial Key Lab. of Visual Media and Multidim. Intelligence; CSSE; Shenzhen University（视觉媒体与多维智能省重点实验室; 计算机科学与工程系; 深圳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对文本引导的3D网格变形难题，PoseAlign提出将其分为全局姿势缩放和局部细节雕刻两阶段的方法。利用拉普拉斯算子及改进的得分蒸馏采样，保持姿势同时雕刻细节，提升了变形可控性，实验验证了该方法在文本对齐和网格质量上的优势。
AI 中文摘要
网格变形是计算机图形学的基石，即改变3D网格顶点位置并保持拓扑结构。尽管近期出现众多文本引导的3D网格变形方法，但将初始网格变形为符合文本提示并保持姿势仍具挑战。本文提出PoseAlign，将文本引导的网格变形分解为全局姿势缩放和局部细节雕刻两个阶段。第一阶段引入拉普拉斯算子实现更高效且平滑的全局变形；第二阶段通过改进得分蒸馏采样提出新颖的姿势对齐SDS损失，在保持原始姿势的同时雕刻精细几何细节。实验证明该方法在文本对齐和网格质量方面具有竞争优势。
英文摘要
Mesh deformation, the process of altering the vertex positions of a 3D mesh while preserving its topological structure, is a cornerstone of computer graphics. Despite the recent emergence of numerous text-guided 3D mesh deformation methods, deforming an initial mesh into one that both adheres to text prompts and preserves its pose remains challenging. This paper proposes PoseAlign, which decomposes text-guided mesh deformation into two stages: global pose scaling and local detail sculpting. Specifically, in the first stage, we introduce the Laplacian as a differentiable mesh representation to enable more efficient yet smoother global deformation. Then, we propose a novel pose-aligned SDS loss by adapting score distillation sampling (SDS) with an attention-sharing mechanism, which sculptures fine-grained geometric details for the deformed mesh while preserving its original pose. PoseAlign significantly enhances the controllability of the overall deformation process, achieving a favorable balance between pose preservation and text alignment. Experiments demonstrate the competitive advantages of our method in text alignment and mesh quality. Code is available at: https://cousingrade6.github.io/PoseAlign
CommentsCGI 2026 Best Paper Award. Project page: https://cousingrade6.github.io/PoseAlign
