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Found 18 papers, 11 papers with code
Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes
When enough annotated training data is available, supervised deep-learning algorithms excel at estimating human body pose and shape using a single camera.
Cache Me if You Can: Accelerating Diffusion Models through Block Caching
However, one of the major drawbacks of diffusion models is that the image generation process is costly.
BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis
To the best of our knowledge, this is the first approach that uses the reverse diffusion process to model full-body tracking as a conditional sequence generation task.
Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
A particular challenge is that only a sparse tracking signal is available from standalone HMDs (Head Mounted Devices), often limited to tracking the user's head and wrists.
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs).
Re-ReND: Real-time Rendering of NeRFs across Devices
Re-ReND is designed to achieve real-time performance by converting the NeRF into a representation that can be efficiently processed by standard graphics pipelines.
MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving
To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it.
Improving Deep Metric Learning by Divide and Conquer
We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts.
Discovering Relationships between Object Categories via Universal Canonical Maps
Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects.
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors
Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification.
A Content Transformation Block For Image Style Transfer
Recent work has significantly improved the representation of color and texture and computational speed and image resolution.
Transferring Dense Pose to Proximal Animal Classes
Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail.
Divide and Conquer the Embedding Space for Metric Learning
Approaches for learning a single distance metric often struggle to encode all different types of relationships and do not generalize well.
Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks
Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention.
A Style-Aware Content Loss for Real-time HD Style Transfer
These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.
Deep Unsupervised Learning of Visual Similarities
Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision.
Deep Unsupervised Similarity Learning using Partially Ordered Sets
Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes.
CliqueCNN: Deep Unsupervised Exemplar Learning
Exemplar learning is a powerful paradigm for discovering visual similarities in an unsupervised manner.
