用关键点驱动压缩扫描人体动态网格，显著降低码率。
KeyNode-Driven Geometry Coding for Real-World Scanned Human Dynamic Mesh Compression
- 以关键点加权计算顶点运动，仅传输关键点变换
- 平均码率降低58.43%，低码率下性能更优
- 适合真实扫描人体动态网格压缩，如虚拟现实应用
真实世界扫描的3D人体动态网格压缩是新兴研究方向，服务于远程呈现、虚拟现实和3D数字流媒体。与拓扑固定的合成动态网格不同，扫描网格在帧间常存在拓扑变化及孔洞、离群点等扫描缺陷，增加预测与压缩难度。此外，人体网格常包含刚性与非刚性运动混合，相比纯刚性物体更难准确预测与编码。为此，我们提出一种针对真实扫描人体动态网格的压缩方法，利用嵌入式关键节点。每个顶点的时序运动被建模为邻近关键节点变换的距离加权组合，只需传输关键节点的变换信息。为提升关键点驱动的预测质量，引入基于八叉树的残差编码方案和双向预测模式，使用双向I帧进行预测。大量实验表明，本方法在所评估序列上平均码率降低58.43%，尤其在低码率下表现优异。
原文摘要 · Abstract (English)
The compression of real-world scanned 3D human dynamic meshes is an emerging research area, driven by applications such as telepresence, virtual reality, and 3D digital streaming. Unlike synthesized dynamic meshes with fixed topology, scanned dynamic meshes often not only have varying topology across frames but also scan defects such as holes and outliers, increasing the complexity of prediction and compression. Additionally, human meshes often combine rigid and non-rigid motions, making accurate prediction and encoding significantly more difficult compared to objects that exhibit purely rigid motion. To address these challenges, we propose a compression method designed for real-world scanned human dynamic meshes, leveraging embedded key nodes. The temporal motion of each vertex is formulated as a distance-weighted combination of transformations from neighboring key nodes, requiring the transmission of solely the key nodes' transformations. To enhance the quality of the KeyNode-driven prediction, we introduce an octree-based residual coding scheme and a Dual-direction prediction mode, which uses I-frames from both directions. Extensive experiments demonstrate that our method achieves significant improvements over the state-of-the-art, with an average bitrate savings of 58.43% across the evaluated sequences, particularly excelling at low bitrates.
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