Neural radiance fields (NeRF) have achieved great success in novel view synthesis and 3D
representation for static scenarios. Existing dynamic NeRFs usually exploit a locally dense …

Neural radiance fields have made a remarkable breakthrough in the novel view synthesis
task at the 3D static scene. However, for the 4D circumstance (eg, dynamic scene), the …

Synthesis of new views in dynamic 3D scenes is a challenging task in 3D vision. However,
most current approaches rely on radiance fields built upon multi-view camera systems for …

We introduce Free3D a simple accurate method for monocular open-set novel view
synthesis (NVS). Similar to Zero-1-to-3 we start from a pre-trained 2D image generator for …

Dynamic neural radiance fields (dynamic NeRFs) have demonstrated impressive results in
novel view synthesis on 3D dynamic scenes. However, they often require complete video …

Modeling dynamic scenes is important for many applications such as virtual reality and
telepresence. Despite achieving unprecedented fidelity for novel view synthesis in dynamic …

With the introduction of Neural Radiance Fields (NeRFs), novel view synthesis has recently
made a big leap forward. At the core, NeRF proposes that each 3D point can emit radiance …

Neural radiance fields have revolutionized the field of novel view synthesis, achieving
remarkable results. However, traditional approaches based on implicit representations …

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged
as a compelling technique for learning to represent 3D scenes from images with the goal of …

Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and
synthesizing novel views, but the computational overhead of NeRF at the inference stage is …