3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis
Real-time free-viewpoint rendering requires balancing multi-camera redundancy with the latency constraints of interactive applications. We address this challenge by combining lightweight geometry with learning and propose 3DTV, a feedforward network for real-time sparse-view interpolation. A Delaunay-based triplet selection ensures angular coverage for each target view. Building on this, we introduce a pose-aware depth module that estimates a coarse-to-fine depth pyramid, enabling efficient feature reprojection and occlusion-aware blending. Unlike methods that require scene-specific optimization, 3DTV runs feedforward without retraining, making it practical for AR/VR, telepresence, and interactive applications. Our experiments on challenging multi-view video datasets demonstrate that 3DTV consistently achieves a strong balance of quality and efficiency, outperforming recent real-time novel-view baselines. Crucially, 3DTV avoids explicit proxies, enabling robust rendering across diverse scenes. This makes it a practical solution for low-latency multi-view streaming and interactive rendering.
  Project Page: https://stefanmschulz.github.io/3DTV_webpage/
69df517ce04d6adeb64ba03f
Stefan Schulz
claimed_verified
2026-04-15T09:58:08.167Z
661abe53eae5a60bb8b5b73e
/avatars/4ced6314b716f6926a0ec83c5576e729.svg
Stefan Schulz
StefanMS
user
StefanMS
69df517ce04d6adeb64ba040
Fernando Edelstein
69df517ce04d6adeb64ba041
Hannah Dröge
69df517ce04d6adeb64ba042
Matthias B. Hullin
69df517ce04d6adeb64ba043
Markus Plack
3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis
Real-time free-viewpoint rendering requires balancing multi-camera redundancy with the latency constraints of interactive applications. We address this challenge by combining lightweight geometry with learning and propose 3DTV, a feedforward network for real-time sparse-view interpolation. A Delaunay-based triplet selection ensures angular coverage for each target view. Building on this, we introduce a pose-aware depth module that estimates a coarse-to-fine depth pyramid, enabling efficient feature reprojection and occlusion-aware blending. Unlike methods that require scene-specific optimization, 3DTV runs feedforward without retraining, making it practical for AR/VR, telepresence, and interactive applications. Our experiments on challenging multi-view video datasets demonstrate that 3DTV consistently achieves a strong balance of quality and efficiency, outperforming recent real-time novel-view baselines. Crucially, 3DTV avoids explicit proxies, enabling robust rendering across diverse scenes. This makes it a practical solution for low-latency multi-view streaming and interactive rendering.
  Project Page: https://stefanmschulz.github.io/3DTV_webpage/
3DTV combines lightweight geometry with learning for real-time sparse-view interpolation, achieving efficient and robust multi-view rendering without scene-specific optimization.
feedforward network sparse-view interpolation Delaunay-based triplet selection pose-aware depth module depth pyramid feature reprojection occlusion-aware blending multi-view video real-time rendering interactive applications