We tackle the task of reconstructing hand-object interactions from short video clips. Given an
input video, our approach casts 3D inference as a per-video optimization and recovers a …

We address the problem of building digital twins of unknown articulated objects from two
RGBD scans of the object at different articulation states. We decompose the problem into …

We address the task of simultaneous part-level reconstruction and motion parameter
estimation for articulated objects. Given two sets of multi-view images of an object in two …

We present CARTO, a novel approach for reconstructing multiple articulated objects from a
single stereo RGB observation. We use implicit object-centric representations and learn a …

In this paper we address the challenge of reconstructing general articulated 3D objects from
a single video. Existing works employing dynamic neural radiance fields have advanced the …

Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a
challenging problem to edit the scenes modeled by NeRF-based methods, especially for …

While NeRF-based human representations have shown impressive novel view synthesis
results, most methods still rely on a large number of images/views for training. In this work …

Abstract We propose Neural 3D Articulated object Prior (NAP), the first 3D deep generative
model to synthesize 3D articulated object models. Despite the extensive research on …

Reconstructing models of the real world, including 3D geometry, appearance, and motion of
real scenes, is essential for computer graphics and computer vision. It enables the …

Understanding the continuous states of objects is essential for task learning and planning in
the real world. However, most existing task learning benchmarks assume discrete (eg …