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 …

Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and
depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets …

We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and
lighting of an articulated animal like a horse given a single test image as input. We present a …

Tracking pixels in videos is typically studied as an optical flow estimation problem, where
every pixel is described with a displacement vector that locates it in the next frame. Even …

Abstract We propose Neural-DynamicReconstruction (NDR), a template-free method to
recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D …

We propose an unsupervised method for 3D geometry-aware representation learning of
articulated objects, in which no image-pose pairs or foreground masks are used for training …

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 …

Capturing the interactions between humans and their environment in 3D is important for
many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D …

Given monocular videos, we build 3D models of articulated objects and environments
whose 3D configurations satisfy dynamics and contact constraints. At its core, our method …