Virtual human is widely employed in various industries, including personal assistance,
intelligent customer service, and online education, thanks to the rapid development of …

Image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of
an object or scene from a set of input images. Learning-based methods have gained …

Abstract Neural Radiance Field training can be accelerated through the use of grid-based
representations in NeRF's learned map** from spatial coordinates to colors and …

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

Neural radiance fields enable state-of-the-art photorealistic view synthesis. However,
existing radiance field representations are either too compute-intensive for real-time …

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

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of
the trade-off between quality and efficiency, eg, MipNeRF presents fine-detailed and anti …

Abstract Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize
images of 3D scenes from novel views. However, they rely upon specialized volumetric …