20,484 research outputs found
Deformable GANs for Pose-based Human Image Generation
In this paper we address the problem of generating person images conditioned
on a given pose. Specifically, given an image of a person and a target pose, we
synthesize a new image of that person in the novel pose. In order to deal with
pixel-to-pixel misalignments caused by the pose differences, we introduce
deformable skip connections in the generator of our Generative Adversarial
Network. Moreover, a nearest-neighbour loss is proposed instead of the common
L1 and L2 losses in order to match the details of the generated image with the
target image. We test our approach using photos of persons in different poses
and we compare our method with previous work in this area showing
state-of-the-art results in two benchmarks. Our method can be applied to the
wider field of deformable object generation, provided that the pose of the
articulated object can be extracted using a keypoint detector.Comment: CVPR 2018 versio
Evaluating Visual Realism in Drawing Areas of Interest on UML Diagrams
Areas of interest (AOIs) are defined as an addition to UML diagrams: groups of elements of system architecture diagrams that share some common property. Some methods have been proposed to automatically draw AOIs on UML diagrams. However, it is not clear how users perceive the results of such methods as compared to human-drawn areas of interest. We present here a process of studying and improving the perceived quality of computer-drawn AOIs. We qualitatively evaluated how users perceive the quality of computer- and human-drawn AOIs, and used these results to improve an existing algorithm for drawing AOIs. Finally, we designed a quantitative comparison for AOI drawings and used it to show that our improved renderings are closer to human drawings than the original rendering algorithm results. The combined user evaluation, algorithmic improvements, and quantitative comparison support our claim of improving the perceived quality of AOIs rendered on UML diagrams.
Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction
The ultimate goal of many image-based modeling systems is to render
photo-realistic novel views of a scene without visible artifacts. Existing
evaluation metrics and benchmarks focus mainly on the geometric accuracy of the
reconstructed model, which is, however, a poor predictor of visual accuracy.
Furthermore, using only geometric accuracy by itself does not allow evaluating
systems that either lack a geometric scene representation or utilize coarse
proxy geometry. Examples include light field or image-based rendering systems.
We propose a unified evaluation approach based on novel view prediction error
that is able to analyze the visual quality of any method that can render novel
views from input images. One of the key advantages of this approach is that it
does not require ground truth geometry. This dramatically simplifies the
creation of test datasets and benchmarks. It also allows us to evaluate the
quality of an unknown scene during the acquisition and reconstruction process,
which is useful for acquisition planning. We evaluate our approach on a range
of methods including standard geometry-plus-texture pipelines as well as
image-based rendering techniques, compare it to existing geometry-based
benchmarks, and demonstrate its utility for a range of use cases.Comment: 10 pages, 12 figures, paper was submitted to ACM Transactions on
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Changes in navigational behaviour produced by a wide field of view and a high fidelity visual scene
The difficulties people frequently have navigating in virtual environments (VEs) are well known. Usually these
difficulties are quantified in terms of performance (e.g., time taken or number of errors made in following a path),
with these data used to compare navigation in VEs to equivalent real-world settings. However, an important cause
of any performance differences is changes in peopleâs navigational behaviour. This paper reports a study that
investigated the effect of visual scene fidelity and field of view (FOV) on participantsâ behaviour in a navigational
search task, to help identify the thresholds of fidelity that are required for efficient VE navigation. With a wide FOV
(144 degrees), participants spent significantly larger proportion of their time travelling through the VE, whereas
participants who used a normal FOV (48 degrees) spent significantly longer standing in one place planning where
to travel. Also, participants who used a wide FOV and a high fidelity scene came significantly closer to conducting
the search "perfectly" (visiting each place once). In an earlier real-world study, participants completed 93% of
their searches perfectly and planned where to travel while they moved. Thus, navigating a high fidelity VE with
a wide FOV increased the similarity between VE and real-world navigational behaviour, which has important
implications for both VE design and understanding human navigation.
Detailed analysis of the errors that participants made during their non-perfect searches highlighted a dramatic
difference between the two FOVs. With a narrow FOV participants often travelled right past a target without it
appearing on the display, whereas with the wide FOV targets that were displayed towards the sides of participants
overall FOV were often not searched, indicating a problem with the demands made by such a wide FOV display
on human visual attention
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