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Found 5 papers, 4 papers with code
Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations
We propose an unsupervised method for 3D geometry-aware representation learning of articulated objects, in which no image-pose pairs or foreground masks are used for training.
Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects
Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies.
Neural Articulated Radiance Field
We present Neural Articulated Radiance Field (NARF), a novel deformable 3D representation for articulated objects learned from images.
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis
The loss is simple yet effective for any type of image generator such as DCGAN and StyleGAN to be conditioned on camera parameters.
Image Generation From Small Datasets via Batch Statistics Adaptation
To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain.
