Time-Archival Camera Virtualization for Sports and Visual Performances

• URL: http://arxiv.org/abs/2602.15181v1
• Date: Mon, 16 Feb 2026 20:39:51 GMT
• Title: Time-Archival Camera Virtualization for Sports and Visual Performances
• Authors: Yunxiao Zhang, William Stone, Suryansh Kumar,
• Abstract summary: Camera virtualization holds transformative potential for visual entertainment, live performances, and sports broadcasting.<n>Recent methods based on 3D Gaussian Splatting (3DGS) for dynamic scenes could offer real-time view-synthesis results.<n>This paper advocates reconsidering a neural volume rendering formulation for camera virtualization and efficient time-archival capabilities.
• Score: 6.771498989384425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camera virtualization -- an emerging solution to novel view synthesis -- holds transformative potential for visual entertainment, live performances, and sports broadcasting by enabling the generation of photorealistic images from novel viewpoints using images from a limited set of calibrated multiple static physical cameras. Despite recent advances, achieving spatially and temporally coherent and photorealistic rendering of dynamic scenes with efficient time-archival capabilities, particularly in fast-paced sports and stage performances, remains challenging for existing approaches. Recent methods based on 3D Gaussian Splatting (3DGS) for dynamic scenes could offer real-time view-synthesis results. Yet, they are hindered by their dependence on accurate 3D point clouds from the structure-from-motion method and their inability to handle large, non-rigid, rapid motions of different subjects (e.g., flips, jumps, articulations, sudden player-to-player transitions). Moreover, independent motions of multiple subjects can break the Gaussian-tracking assumptions commonly used in 4DGS, ST-GS, and other dynamic splatting variants. This paper advocates reconsidering a neural volume rendering formulation for camera virtualization and efficient time-archival capabilities, making it useful for sports broadcasting and related applications. By modeling a dynamic scene as rigid transformations across multiple synchronized camera views at a given time, our method performs neural representation learning, providing enhanced visual rendering quality at test time. A key contribution of our approach is its support for time-archival, i.e., users can revisit any past temporal instance of a dynamic scene and can perform novel view synthesis, enabling retrospective rendering for replay, analysis, and archival of live events, a functionality absent in existing neural rendering approaches and novel view synthesis...

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