424 research outputs found
MoSculp: Interactive Visualization of Shape and Time
We present a system that allows users to visualize complex human motion via
3D motion sculptures---a representation that conveys the 3D structure swept by
a human body as it moves through space. Given an input video, our system
computes the motion sculptures and provides a user interface for rendering it
in different styles, including the options to insert the sculpture back into
the original video, render it in a synthetic scene or physically print it.
To provide this end-to-end workflow, we introduce an algorithm that estimates
that human's 3D geometry over time from a set of 2D images and develop a
3D-aware image-based rendering approach that embeds the sculpture back into the
scene. By automating the process, our system takes motion sculpture creation
out of the realm of professional artists, and makes it applicable to a wide
range of existing video material.
By providing viewers with 3D information, motion sculptures reveal space-time
motion information that is difficult to perceive with the naked eye, and allow
viewers to interpret how different parts of the object interact over time. We
validate the effectiveness of this approach with user studies, finding that our
motion sculpture visualizations are significantly more informative about motion
than existing stroboscopic and space-time visualization methods.Comment: UIST 2018. Project page: http://mosculp.csail.mit.edu
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
Depth estimation from a single image is a fundamental problem in computer
vision. In this paper, we propose a simple yet effective convolutional spatial
propagation network (CSPN) to learn the affinity matrix for depth prediction.
Specifically, we adopt an efficient linear propagation model, where the
propagation is performed with a manner of recurrent convolutional operation,
and the affinity among neighboring pixels is learned through a deep
convolutional neural network (CNN). We apply the designed CSPN to two depth
estimation tasks given a single image: (1) To refine the depth output from
state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth
samples to a dense depth map by embedding the depth samples within the
propagation procedure. The second task is inspired by the availability of
LIDARs that provides sparse but accurate depth measurements. We experimented
the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2
and KITTI, where we show that our proposed approach improves in not only
quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5
times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201
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