# CPSL: Representing Volumetric Video via Content-Promoted Scene Layers

arXiv: https://arxiv.org/abs/2511.14927 · HTML: https://mlsanity.com/paper/2511.14927/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 22 of 22)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Single-View View Synthesis with Multiplane Images](https://mlsanity.com/paper/2004.11364/) (2020)
- [Shape of Motion: 4D Reconstruction from a Single Video](https://mlsanity.com/paper/2407.13764/) (2024)
- [3D Photography using Context-aware Layered Depth Inpainting](https://mlsanity.com/paper/2004.04727/) (2020)
- [Monocular Dynamic View Synthesis: A Reality Check](https://mlsanity.com/paper/2210.13445/) (2022)
- [Extreme View Synthesis](https://mlsanity.com/paper/1812.04777/) (2018)
- [Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images](https://mlsanity.com/paper/2205.11733/) (2022)
- [Representing Volumetric Videos as Dynamic MLP Maps](https://mlsanity.com/paper/2304.06717/) (2023)
- [V2M4: 4D Mesh Animation Reconstruction from a Single Monocular Video](https://mlsanity.com/paper/2503.09631/) (2025)
- [Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction](https://mlsanity.com/paper/2303.05937/) (2023)
- [Adaptive Multiplane Image Generation from a Single Internet Picture](https://mlsanity.com/paper/2011.13317/) (2020)
- [From Capture to Display: A Survey on Volumetric Video](https://mlsanity.com/paper/2309.05658/) (2023)
- [TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering](https://mlsanity.com/paper/2303.15060/) (2023)
- [Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction](https://mlsanity.com/paper/2308.07578/) (2023)
- [FSVVD: A Dataset of Full Scene Volumetric Video](https://mlsanity.com/paper/2303.03599/) (2023)
- [LiveVV: Human-Centered Live Volumetric Video Streaming System](https://mlsanity.com/paper/2310.08205/) (2023)
- [AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2402.03093/) (2024)

## 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.