Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

We present a new test-time optimization method for estimating dense and long-range motion
from a video sequence. Prior optical flow or particle video tracking algorithms typically …

Building animatable 3D models is challenging due to the need for 3D scans, laborious
registration, and manual rigging. Recently, differentiable rendering provides a pathway to …

We present a method for reconstructing articulated 3D models from videos in real-time,
without test-time optimization or manual 3D supervision at training time. Prior work often …

Training a 3D human keypoint detector from point clouds in a supervised manner requires
large volumes of high quality labels. While it is relatively easy to capture large amounts of …

Can computers perceive the physical properties of objects solely through vision? Research
in cognitive science and vision science has shown that humans excel at identifying materials …

Single-view RGB-D human reconstruction with implicit functions is often formulated as per-
point classification. Specifically, a set of 3D locations within the view-frustum of the camera …

We build rearticulable models for arbitrary everyday man-made objects containing an
arbitrary number of parts that are connected together in arbitrary ways via 1-degree-of …

Abstract We present Bayesian Diffusion Models (BDM) a prediction algorithm that performs
effective Bayesian inference by tightly coupling the top-down (prior) information with the …

Animating an object in 3D often requires an articulated structure, eg a kinematic chain or
skeleton of the manipulated object with proper skinning weights, to obtain smooth …