Capture Stage Matting: Challenges, Approaches, and Solutions for Offline and Real-Time Processing

TL;DR

The paper addresses capture-stage matting challenges where reflections, shadows, and lighting introduce background perturbations that violate simple compositing models. It proposes a two-phase pipeline that combines a background-informed offline teacher refined with sparse scribbles and a lightweight real-time student distilled from teacher outputs, enabling robust offline and real-time matting without heavy per-frame annotations. Validation leverages a diffusion-model-based objective and demonstrates improved alpha masks and downstream NeRF reconstructions, highlighting practical gains for controlled-environment capture workflows. The work offers concrete setup guidelines and a scalable distillation framework to balance accuracy and speed in production environments.

Abstract

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.

Figures (9)

• Figure 1: We present a pipeline that adapts state-of-the-art matting approaches to the challenges of capture stage setups leveraging background information that is readily available in these controlled environments (left). We demonstrate how to guide an offline matting method via sparse scribbles (center) and use the improved predictions from the offline method to teach a lightweight real-time matting method (right), enabling better performance across both offline and real-time workflows.
• Figure 2: Challenging conditions, like reflective materials of the capture stage setup (left), as well as cast shadows (center), can lead to alterations of the background image and significantly impact matting (right).
• Figure 3: Ambiguities in foreground and background information can be caused by lighting in the background (left) and dark objects against similarly dark backgrounds (right).
• Figure 4: Illustration of the student-teacher model refinement process. The teacher network is fine-tuned on specific failure cases provided by the user to improve its predictions, even on unseen datasets (left). The real-time student model is subsequently fine-tuned based on corrected predictions of the improved teacher model, ensuring better generalization and performance (right).
• Figure 5: Influence of noise in the input images on matting. The left image shows a matting result without introducing noise during training, whereas the right image shows the result with noise added to the augmentation phase during training.