Americas, Special Report, Entertainment

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We also present a module for fine-grained registration between pixels. The canonical space by matching to an implicit feature volume. To jointly optimize over a large number of video frames with a manageable computational cost, we actively sample pixels locations based on uncertainty. Our contributions are summarized as follows: (1) BANMo allows for high-fidelity reconstruction of animatable 3D models from casual videos containing thousands of frames; (2) To handle large articulated body motions, we propose a novel neural blend skinning model (Sec. 3.2); (3) To register pixels from different frames, we introduce a method for canonical embedding matching and learning (Sec. 3.3); (4) To robustify the optimization, we propose an pipeline for rough root body pose initialization (Sec. 3.4); (5) Finally, to reconstruct detailed geometry, we introduce an active sampling strategy for inverse rendering (Sec. Human and animal body models. Although parametric body models achieve great success in reconstructing categories for which large amounts of ground-truth 3D data are available (mostly in the case of human reconstruction), it is challenging to apply the same methodology to categories with limited 3D data, such as animals and humans in diverse sets of clothing.




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