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In addition to being beneficial to many dynamicmulti-view scenarios this also enables larger scenes where such increased precision can compensate for the reduced spatial resolution per image frame.The source code implementing the different reconstruction methods is released to the community at.
#Paralax backrounds in hexels how to
Once we obtain this representation at every frame of the captured sequence, we discuss how to exploittemporal redundancy for precision refinement by propagating shape detailsthrough adjacent frames. We thenexplain how to use this learning strategy to robustly build a shape representation, from which can be extracted a 3D model.
We examine in a first contribution whether theseimprovements transfer to the more general and complex case of live performance capture, where a diverse set of additional difficulties arise. Interestingly, very recent works have re-examined stereo and MVS byintroducing features and similarity functions automatically inferred usingdeep learning, the main promise of this type of method being to includebetter data-driven priors. First, Multi-view stereo (MVS)based methods have attained a good level of quality with pipelines thattypically comprise feature extraction, matching stages and 3D shape inference.
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An essential and still improvable aspect in this matter is the fidelity and quality of the recovered shapes, ourgoal in this work.We present a full reconstruction pipeline suited for this scenario, towhich we contributed in many aspects. Typical challenges for these capture situationsinclude smaller visual projection areas of objects of interest due to widernecessary fields of view for capturing motion occlusion and self-occlusionof several subjects interacting together lack of texture content typical ofreal-life subject appearance and clothing or motion blur with fast movingsubjects such as sport action scenes. We especially target real life performance capture, containing complex surface details.
This manuscript aims at presenting the problem of 4D shapereconstruction from multi-view RGB images, which is one way to createsuch content. In particular, the rise of virtual and augmented reality fuels an increased need forcontent suitable to these new media including 3D contents obtained fromreal scenes, as the ability to record a live performance and replay it from anygiven point of view allows the user to experience a realistic and immersiveenvironment. Recent advances in acquisition and processing technologies lead to the fastgrowth of a major branch in media production: volumetric video.
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