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Found 12 papers, 2 papers with code
3D Gaussian Splatting as Markov Chain Monte Carlo
While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which does not always generalize and may lead to poor-quality renderings.
Accelerating Neural Field Training via Soft Mining
We present an approach to accelerate Neural Field training by efficiently selecting sampling locations.
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
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts.
K-convexity shape priors for segmentation
As shown in the paper, for many forms of convexity our regularization model is significantly more descriptive for any given k. Our shape prior is useful in practice, e. g. in biomedical applications, and its optimization is robust to local minima.
Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)
We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments.
Hedgehog Shape Priors for Multi-Object Segmentation
In contrast to star-convexity, the tightness of our normal constraint can be changed giving better control over allowed shapes.
A-expansion for multiple "hedgehog" shapes
A single click and +/-90 degrees normal orientation constraints reduce our hedgehog prior to star-convexity.
Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions
Many standard optimization methods for segmentation and reconstruction compute ML model estimates for appearance or geometry of segments, e. g. Zhu-Yuille 1996, Torr 1998, Chan-Vese 2001, GrabCut 2004, Delong et al. 2012.
Energy Based Multi-model Fitting & Matching for 3D Reconstruction
Standard geometric model fitting methods take as an input a fixed set of feature pairs greedily matched based only on their appearances.
Joint optimization of fitting & matching in multi-view reconstruction
In contrast, we solve feature matching and multi-model fitting problems in a joint optimization framework.
