104 research outputs found
PSC 352.01: American Political Thought
In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multiscale strategy to learn the pixel-level correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches and multi-view line drawings rendered from multiple 3D shape datasets
VCoach: A Customizable Visualization and Analysis System for Video-based Running Coaching
Videos are accessible media for analyzing sports postures and providing
feedback to athletes. Existing video-based coaching systems often present
feedback on the correctness of poses by augmenting videos with visual markers
either manually by a coach or automatically by computing key parameters from
poses. However, previewing and augmenting videos limit the analysis and
visualization of human poses due to the fixed viewpoints, which confine the
observation of captured human movements and cause ambiguity in the augmented
feedback. Besides, existing sport-specific systems with embedded bespoke pose
attributes can hardly generalize to new attributes; directly overlaying two
poses might not clearly visualize the key differences that viewers would like
to pursue. To address these issues, we analyze and visualize human pose data
with customizable viewpoints and attributes in the context of common
biomechanics of running poses, such as joint angles and step distances. Based
on existing literature and a formative study, we have designed and implemented
a system, VCoach, to provide feedback on running poses for amateurs. VCoach
provides automatic low-level comparisons of the running poses between a novice
and an expert, and visualizes the pose differences as part-based 3D animations
on a human model. Meanwhile, it retains the users' controllability and
customizability in high-level functionalities, such as navigating the viewpoint
for previewing feedback and defining their own pose attributes through our
interface. We conduct a user study to verify our design components and conduct
expert interviews to evaluate the usefulness of the system
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