Capture Stage Matting: Challenges, Approaches, and Solutions for Offline and Real-Time Processing
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Abstract (arXiv)
Capture stages are high-end sources of state-of-the-art recordings for downstream applications in movies, games, and other media. One crucial step in almost all pipelines is matting, i.e., separating captured performances from the background. While common matting algorithms deliver remarkable performance in other applications like teleconferencing and mobile entertainment, we found that they struggle significantly with the peculiarities of capture stage content. The goal of our work is to share insights into those challenges as a curated list of these characteristics along with a constructive discussion for proactive intervention and present a guideline to practitioners for an improved workflow to mitigate unresolved challenges. To this end, we also demonstrate an efficient pipeline to adapt state-of-the-art approaches to such custom setups without the need for extensive annotations, both offline and real-time. For an objective evaluation, we introduce a validation methodology using a state-of-the-art diffusion model to demonstrate the benefits of our approach.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 35 references that are in the map. First posted on arXiv 10 Jul 2025.
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28 of 35 references.
References · 28
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
- 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 596
- 2020Real-Time High-Resolution Background Mattingcited by 172
- 2017Deep Image Mattingcited by 150
- 2021Robust High-Resolution Video Matting with Temporal Guidancecited by 136
- 2020MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decompositioncited by 127
- 2020Background Matting: The World is Your Green Screencited by 104
- 2020Bridging Composite and Real: Towards End-to-end Deep Image Mattingcited by 89
- 2023ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformerscited by 56
- 2023Neural Haircut: Prior-Guided Strand-Based Hair Reconstructioncited by 43
- 2023Matte Anything: Interactive Natural Image Matting with Segment Anything Modelscited by 39
- 2023Matting Anythingcited by 30
16 more
- 2024MaGGIe: Masked Guided Gradual Human Instance Mattingcited by 18
- 2024Matting by Generationcited by 13
- 2023Deep Image Matting: A Comprehensive Surveycited by 12
- 2023Diffusion for Natural Image Mattingcited by 11
- 2023Adaptive Human Matting for Dynamic Videoscited by 7
- 2023EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Mattingcited by 4
- 2024Boosting General Trimap-free Matting in the Real-World Imagecited by 4
- 2024Training Matting Models without Alpha Labelscited by 4
- 2024In-Context Mattingcited by 3
- 2024Learning Trimaps via Clicks for Image Mattingcited by 3
- 2024Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Mattingcited by 3
- 2022Privileged Prior Information Distillation for Image Mattingcited by 2
- 2022Infusing Definiteness into Randomness: Rethinking Composition Styles for Deep Image Mattingcited by 2
- 2023Disentangled Pre-training for Image Mattingcited by 2
- 2023Color-aware Deep Temporal Backdrop Duplex Matting Systemcited by 1
- 2024VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priorscited by 1
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- 2025Generative Video Matting
- 2025SDMatte: Grafting Diffusion Models for Interactive Matting
- 2026ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation
- 2025VRMDiff: Text-Guided Video Referring Matting Generation of Diffusion
- 2026RenderMatte: Exact-Alpha Rendering and Group-Relative Alignment for Image Matting
- 2024DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
- 2024Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting