Indoor environment model reconstruction has emerged as a significant and challenging task
in terms of the provision of a semantically rich and geometrically accurate indoor model …

Human volumetric capture is a long-standing topic in computer vision and computer
graphics. Although high-quality results can be achieved using sophisticated off-line systems …

Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly
on modeling static objects captured under multi-view settings where real-time rendering can …

We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal
representation of a dynamic scene from a set of RGB images. Key to our approach is the use …

Generating free-viewpoint videos is critical for immersive VR/AR experience, but recent
neural advances still lack the editing ability to manipulate the visual perception for large …

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input
image. The training data consists of a texture-mapped 3D object model or images of the …

Deep learning based 3D reconstruction techniques have recently achieved impressive
results. However, while state-of-the-art methods are able to output complex 3D geometry, it …

We present the first real-time human performance capture approach that reconstructs dense,
space-time coherent deforming geometry of entire humans in general everyday clothing …

Estimating geometry from dynamic scenes, where objects move and deform over time,
remains a core challenge in computer vision. Current approaches often rely on multi-stage …

Spectacular progress in the information processing sciences (machine learning, wearable
sensors) promises to revolutionize the study of cognitive development. Here, we analyse the …