Papers › Continuous Surface Embeddings

Continuous Surface Embeddings

24 Nov 2020NeurIPS 2020 12arXiv:2011.12438archive 2025-07-28

Natalia Neverova, David Novotny, Vasil Khalidov, Marc Szafraniec, Patrick Labatut, Andrea Vedaldi

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 object types (i.e., humans), often with significant manual work involved. However, scaling the geometry understanding to all objects in nature requires more automated approaches that can also express correspondences between related, but geometrically different objects. To this end, we propose a new, learnable image-based representation of dense correspondences. Our model predicts, for each pixel in a 2D image, an embedding vector of the corresponding vertex in the object mesh, therefore establishing dense correspondences between image pixels and 3D object geometry. We demonstrate that the proposed approach performs on par or better than the state-of-the-art methods for dense pose estimation for humans, while being conceptually simpler. We also collect a new in-the-wild dataset of dense correspondences for animal classes and demonstrate that our framework scales naturally to the new deformable object categories.

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zetyquickly/DensePoseFnL mentioned on GitHubpytorch report
zhongshsh/4d-animal mentioned on GitHubpytorch report

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CseProcessing zhongshsh/4d-animal/cse_embedding/cse_embedder.py community (archive-listed) ran Apache-2.0 (permissive) · b597401c9c6ca9b2 · report
get_lbo_from_name zhongshsh/4d-animal/cse_embedding/cse_embedder.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · 3a59c264202f943d · report
get_mapping_from_name zhongshsh/4d-animal/cse_embedding/cse_embedder.py community (archive-listed) ran · honoured contract Apache-2.0 (permissive) · d730689f8c32a54f · report
normalize_numpy zhongshsh/4d-animal/cse_embedding/cse_embedder.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · 13134f47b0fecb82 · report
resize_torch zhongshsh/4d-animal/cse_embedding/cse_embedder.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · 1567009b1e58288f · report
map_X_from_source_to_destination zhongshsh/4d-animal/cse_embedding/cse_embedder.py community (archive-listed) unverified Apache-2.0 (permissive) · ec94e6d72be4aaff · report

Tasks

Animal Pose EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Pose Estimation DensePose-COCO DP-RCNN-DeepLab (ResNet-101) AP 68.0 #1 of 4 Archive leaderboard report

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Methods

ConvolutionMask R-CNNRPNRoIAlignSoftmax

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