NIVM: Real-time View Morphing via Neural Implicit Function

We present NIVM, a lightweight and efficient view morphing framework that learns coordinate transforms between views, enabling real-time, user-controlled perspective shifts on resource-constrained devices. Unlike existing view interpolation methods that compromise visual quality or require high data overhead, NIVM integrates seamlessly into multi-view video streams as compact metadata per frame, enabling the synthesis of high-quality intermediate views and interactive transitions from sparse viewpoints. To avoid dependence on explicit 3D geometry, which may be unavailable, we introduce a dual-branch training strategy: a teacher network operates in rectified stereo space to supervise the morpher in the original image domain. By inheriting the monotonicity constraints of epipolar geometry, our morphing network produces visually plausible pixel flows while avoiding the reprojection artifacts prevalent in depth-based methods. Compared to recent pose-free sparse-view Gaussian Splatting approaches, NIVM achieves competitive results without the need to construct or transmit volumetric representations. Experiments show that NIVM achieves the lowest memory footprint, highest inference efficiency, and top-tier visual quality across multiple benchmark datasets.