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Found 4 papers, 3 papers with code
Unsupervised Keypoints from Pretrained Diffusion Models
Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures, but performance is yet to match the supervised counterpart, making their practicability questionable.
Ranked #1 on
Unsupervised Human Pose Estimation
on Tai-Chi-HD
Unsupervised Semantic Correspondence Using Stable Diffusion
Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images.
Ranked #1 on
Semantic correspondence
on PF-WILLOW
CN-DHF: Compact Neural Double Height-Field Representations of 3D Shapes
We introduce CN-DHF (Compact Neural Double-Height-Field), a novel hybrid neural implicit 3D shape representation that is dramatically more compact than the current state of the art.
A Simple Method to Boost Human Pose Estimation Accuracy by Correcting the Joint Regressor for the Human3.6m Dataset
While the quality of this pseudo-ground-truth is challenging to assess due to the lack of actual ground-truth SMPL, with the Human 3. 6m dataset, we qualitatively show that our joint locations are more accurate and that our regressor leads to improved pose estimations results on the test set without any need for retraining.
