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CPSL: Representing Volumetric Video via Content-Promoted Scene Layers

arXiv 2511.14927 ↗First posted 18 Nov 2025cs.CV cs.MMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 18 Nov 2025.

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22 of 36 references.

References · 22

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  4. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
  5. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,144
  6. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  7. 2020Single-View View Synthesis with Multiplane Imagescited by 263
  8. 2024Shape of Motion: 4D Reconstruction from a Single Videocited by 247
  9. 20203D Photography using Context-aware Layered Depth Inpaintingcited by 206
  10. 2022Monocular Dynamic View Synthesis: A Reality Checkcited by 199
  11. 2018Extreme View Synthesiscited by 94
  12. 2022Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Imagescited by 59
10 more
  1. 2023Representing Volumetric Videos as Dynamic MLP Mapscited by 51
  2. 2025V2M4: 4D Mesh Animation Reconstruction from a Single Monocular Videocited by 32
  3. 2023Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstructioncited by 12
  4. 2020Adaptive Multiplane Image Generation from a Single Internet Picturecited by 9
  5. 2023From Capture to Display: A Survey on Volumetric Videocited by 9
  6. 2023TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Renderingcited by 7
  7. 2023Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Predictioncited by 6
  8. 2023FSVVD: A Dataset of Full Scene Volumetric Videocited by 5
  9. 2023LiveVV: Human-Centered Live Volumetric Video Streaming Systemcited by 4
  10. 2024AI-Enhanced Virtual Reality in Medicine: A Comprehensive Surveycited by 2

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