Novel view synthesis from limited observations remains a crucial and ongoing challenge. In
the realm of NeRF-based few-shot view synthesis, there is often a trade-off between the …

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 …

We introduce pixelSplat a feed-forward model that learns to reconstruct 3D radiance fields
parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time …

Virtual reality and augmented reality (XR) bring increasing demand for 3D content
generation. However, creating high-quality 3D content requires tedious work from a human …

Neural radiance fields (NeRFs) refer to a suit of deep neural networks that are used to learn
and represent objects or scenes. Generally speaking, NeRFs have five main characters …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

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

Abstract We present Viewset Diffusion, a diffusion-based generator that outputs 3D objects
while only using multi-view 2D data for supervision. We note that there exists a one-to-one …

Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense
covers largely prohibits its wider applications. While several recent works have attempted to …

We introduce an approach that creates animatable human avatars from monocular videos
using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields …