Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs
We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model. Our approach's key novelty lies in formulating dense correspondence computation as initializing and synchronizing local transformations between the scan and the template model. We introduce an optimization formulation for synchronizing transformations among a graph of the input scan, which automatically enforces smoothness of correspondences and recovers the underlying articulated deformations. We then show how to convert the iterative optimization procedure among a graph of the input scan into an end-to-end trainable network. The network design utilizes additional trainable parameters to break the barrier of the original optimization formulation's exact and robust recovery conditions. Experimental results on benchmark datasets demonstrate that our approach considerably outperforms baseline approaches in accuracy and robustness.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Direct Dense Pose Estimation
Dense human pose estimation is the problem of learning dense correspondences between RGB images and the surfaces of human bodies, which finds various applications, such as human body reconstruction, human pose transfer, …
Action RecognitionPose EstimationPose TransferTemporal Action LocalizationLearning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations
We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE) -- originally designed for …
Dimensionality ReductionPoint cloud reconstructionSelf-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects
Learning 3D shape representation with dense correspondence for deformable objects is a fundamental problem in computer vision. Existing approaches often need additional annotations of specific semantic domain, e.g., skel…
3D Shape RepresentationSelf-Supervised LearningContinuous Surface Embeddings
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories. While this problem has been considered before, solutions so far have been rather ad-hoc for specific …
Animal Pose EstimationPose EstimationA generic method of wearable items virtual try-on
Virtual try-on synthesizes garments for the target bodies in 2D/3D domains. Even though existing virtual try-on methods focus on redressing garments, the virtual try-on hair, shoes and wearable accessories are still unde…
Virtual Try-on