We address the problem of synthesizing novel views from a monocular video depicting a
complex dynamic scene. State-of-the-art methods based on temporally varying Neural …

Dynamic radiance field reconstruction methods aim to model the time-varying structure and
appearance of a dynamic scene. Existing methods, however, assume that accurate camera …

Novel view synthesis of dynamic scenes has been an intriguing yet challenging problem.
Despite recent advancements simultaneously achieving high-resolution photorealistic …

Although neural radiance fields (NeRF) have shown impressive advances in novel view
synthesis, most methods require multiple input images of the same scene with accurate …

This paper introduces a novel representation of volumetric videos for real-time view
synthesis of dynamic scenes. Recent advances in neural scene representations …

The introduction of neural radiance fields has greatly improved the effectiveness of view
synthesis for monocular videos. However, existing algorithms face difficulties when dealing …

Novel view synthesis from an in-the-wild video is difficult due to challenges like scene
dynamics and lack of parallax. While existing methods have shown promising results with …

Novel view synthesis has shown rapid progress recently, with methods capable of producing
increasingly photorealistic results. 3D Gaussian Splatting has emerged as a promising …

Neural rendering is fuelling a unification of learning, 3D geometry and video understanding
that has been waiting for more than two decades. Progress, however, is still hampered by a …

Reconstructing models of the real world, including 3D geometry, appearance, and motion of
real scenes, is essential for computer graphics and computer vision. It enables the …