Dec 6, 2021
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We introduce ViSER, a method for recovering articulated 3D shapes and dense 3D part trajectories from monocular videos. Previous work on high-quality reconstruction of dynamic 3D shapes typically relies on multiple camera views, strong category-specific priors, or 2D keypoint supervision. We show that none of these are required with reliable estimation of long-range 2D point correspondences, making use of only 2D object masks and two-frame optical flow as inputs. ViSER infers correspondences by matching 2D pixels to a canonical, deformable 3D mesh via video-specific surface embeddings that capture the pixel appearance of each surface point. These surface embeddings behave as a continuous set of keypoint descriptors defined over the mesh surface, which can be used to establish dense long-range correspondences across pixels. These surface embeddings are implemented via surface-coordinate-based MLPs that are fit to each video via contrastive reconstruction losses. Experimental results show that ViSER compares favorably against prior work on challenging videos of humans with loose clothing and unusual poses as well as animals videos from DAVIS and YTVOS.We introduce ViSER, a method for recovering articulated 3D shapes and dense 3D part trajectories from monocular videos. Previous work on high-quality reconstruction of dynamic 3D shapes typically relies on multiple camera views, strong category-specific priors, or 2D keypoint supervision. We show that none of these are required with reliable estimation of long-range 2D point correspondences, making use of only 2D object masks and two-frame optical flow as inputs. ViSER infers correspondences by…
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Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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