88 citations · 423 across the 74 of their papers we have counts for
9 papers · 1 filter
Self-Learning Transformations for Improving Gaze and Head Redirection
Yufeng Zheng, Seonwook Park, Xucong Zhang +2
Many computer vision tasks rely on labeled data. Rapid progress in generative modeling has led to the ability to synthesize photorealistic images. However, controlling specific asp…
Convolutional Autoencoders for Human Motion Infilling
Manuel Kaufmann, Emre Aksan, Jie Song +3
In this paper we propose a convolutional autoencoder to address the problem of motion infilling for 3D human motion data. Given a start and end sequence, motion infilling aims to c…
Human Body Model Fitting by Learned Gradient Descent
Jie Song, Xu Chen, Otmar Hilliges
We propose a novel algorithm for the fitting of 3D human shape to images. Combining the accuracy and refinement capabilities of iterative gradient-based optimization techniques wit…
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
Xu Chen, Zijian Dong, Jie Song +2
Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine…
Towards End-to-end Video-based Eye-Tracking
Seonwook Park, Emre Aksan, Xucong Zhang +1
Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors. Achieving high accuracy typically requires labeled data fr…
CoSE: Compositional Stroke Embeddings
Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi +1
We present a generative model for complex free-form structures such as stroke-based drawing tasks. While previous approaches rely on sequence-based models for drawings of basic obj…
A Spatio-temporal Transformer for 3D Human Motion Prediction
Emre Aksan, Manuel Kaufmann, Peng Cao +1
We propose a novel Transformer-based architecture for the task of generative modelling of 3D human motion. Previous work commonly relies on RNN-based models considering shorter for…
Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints
Adrian Spurr, Umar Iqbal, Pavlo Molchanov +2
Estimating 3D hand pose from 2D images is a difficult, inverse problem due to the inherent scale and depth ambiguities. Current state-of-the-art methods train fully supervised deep…
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning
Sammy Christen, Lukas Jendele, Emre Aksan +1
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios. Functional d…