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Found 7 papers, 4 papers with code
Learning Generalizable Feature Fields for Mobile Manipulation
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects.
Learning Part Segmentation from Synthetic Animals
We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution.
ActorsNeRF: Animatable Few-shot Human Rendering with Generalizable NeRFs
While NeRF-based human representations have shown impressive novel view synthesis results, most methods still rely on a large number of images / views for training.
CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs
We represent the correspondence maps of different images as warped coordinate frames transformed from a canonical coordinate frame, i. e., the correspondence map, which describes the structure (e. g., the shape of a face), is controlled via a transformation.
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
To deal with the large shape variance, we introduce Articulated Signed Distance Functions (A-SDF) to represent articulated shapes with a disentangled latent space, where we have separate codes for encoding shape and articulation.
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area.
Learning from Synthetic Animals
Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data.
