MeshAvatar：从多视角视频学习高质量三角形人体化身
MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos
AI总结：
针对NeRF类化身学习方法与传统图形流水线不兼容的问题，提出MeshAvatar流水线，用SDF提取的显式三角形网格加姿态条件隐式材质场表示化身，结合物理渲染、2D UNet和伪法线监督，实现高质量几何重建与材质分解，支持编辑、重光照等操作。
摘要：
我们提出了一种从多视角视频学习高质量三角形人体化身的新型流水线。现有的化身学习方法通常基于神经辐射场（Neural Radiance Fields, NeRF），这类方法与传统图形流水线不兼容，给编辑、不同环境下合成等操作带来了极大挑战。为克服这些局限，我们的方法用从隐式有符号距离场（Signed Distance Field, SDF）提取的显式三角形网格表示化身，并辅以以给定姿态为条件的隐式材质场。借助这种三角形化身表示，我们引入基于物理的渲染以准确分解几何与纹理。为增强几何和外观细节，我们进一步采用2D UNet作为网络骨干，并引入伪法线真值作为额外监督。实验表明，我们的方法能学习到具备高质量几何重建和合理材质分解效果的三角形化身，天然支持编辑、操作或重光照操作。
英文摘要：
We present a novel pipeline for learning high-quality triangular human avatars from multi-view videos. Recent methods for avatar learning are typically based on neural radiance fields (NeRF), which is not compatible with traditional graphics pipeline and poses great challenges for operations like editing or synthesizing under different environments. To overcome these limitations, our method represents the avatar with an explicit triangular mesh extracted from an implicit SDF field, complemented by an implicit material field conditioned on given poses. Leveraging this triangular avatar representation, we incorporate physics-based rendering to accurately decompose geometry and texture. To enhance both the geometric and appearance details, we further employ a 2D UNet as the network backbone and introduce pseudo normal ground-truth as additional supervision. Experiments show that our method can learn triangular avatars with high-quality geometry reconstruction and plausible material decomposition, inherently supporting editing, manipulation or relighting operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
