CPSL：基于内容增强场景层的体积视频表示方法
CPSL: Representing Volumetric Video via Content-Promoted Scene Layers
AI总结：
针对现有体积视频表示采集、计算、渲染成本高的问题，提出紧凑2.5D表示CPSL，通过显著性引导分层、轻量编解码实现高保真视差渲染，大幅降低成本，为可扩展沉浸式媒体提供实用方案。
摘要：
体积视频通过支持自由视角探索与真实运动视差，可实现沉浸式交互视觉体验。但现有体积表示方案从显式点云到隐式神经场，在采集、计算与渲染环节成本高昂，限制了其在按需视频场景的可扩展性，也降低了实时通信场景的可行性。为填补这一缺口，我们提出内容增强场景层（Content-Promoted Scene Layers, CPSL），这是一种紧凑的2.5D视频表示，可将体积视频的感知优势引入传统2D内容。在逐帧深度与内容显著性引导下，CPSL将每一帧分解为少量几何一致的图层，这些图层配备软alpha通道带与边缘深度缓存，可共同保留遮挡顺序与边界连续性。这类轻量化、可2D编码的资产无需昂贵的3D重建，通过深度加权变形与由前至后的alpha合成即可实现视差校正的新视角合成。时序维度上，CPSL利用运动引导传播与逐图层编码维持帧间一致性，支持通过标准视频编解码器实现实时播放。在多个基准测试中，CPSL相比基于图层的基线方法与神经场基线方法取得了更优的感知质量与边界保真度，同时将存储与渲染成本降低数倍。我们的方法为从2D视频向可扩展2.5D沉浸式媒体演进提供了可行路径。
英文摘要：
Volumetric video enables immersive and interactive visual experiences by supporting free viewpoint exploration and realistic motion parallax. However, existing volumetric representations from explicit point clouds to implicit neural fields, remain costly in capture, computation, and rendering, which limits their scalability for on-demand video and reduces their feasibility for real-time communication. To bridge this gap, we propose Content-Promoted Scene Layers (CPSL), a compact 2.5D video representation that brings the perceptual benefits of volumetric video to conventional 2D content. Guided by per-frame depth and content saliency, CPSL decomposes each frame into a small set of geometry-consistent layers equipped with soft alpha bands and an edge-depth cache that jointly preserve occlusion ordering and boundary continuity. These lightweight, 2D-encodable assets enable parallax-corrected novel-view synthesis via depth-weighted warping and front-to-back alpha compositing, bypassing expensive 3D reconstruction. Temporally, CPSL maintains inter-frame coherence using motion-guided propagation and per-layer encoding, supporting real-time playback with standard video codecs. Across multiple benchmarks, CPSL achieves superior perceptual quality and boundary fidelity compared with layer-based and neural-field baselines while reducing storage and rendering cost by several folds. Our approach offer a practical path from 2D video to scalable 2.5D immersive media.
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。
