Search Results for author:
Found 19 papers, 11 papers with code
Optimizing Diffusion Noise Can Serve As Universal Motion Priors
We propose Diffusion Noise Optimization (DNO), a new method that effectively leverages existing motion diffusion models as motion priors for a wide range of motion-related tasks.
Physically Plausible Full-Body Hand-Object Interaction Synthesis
Through a hierarchical framework, we first learn skill priors for both body and hand movements in a decoupled setting.
Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors
We further qualitatively evaluate the effectiveness of our method on real images and demonstrate its generalizability towards interaction types and object categories.
LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space
To mitigate this asymmetry, we introduce a prior model that is conditioned on the runtime inputs and tie this prior space to the 3D face model via a normalizing flow in the latent space.
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.
Convolutional Autoencoders for Human Motion Infilling
At the heart of our approach lies the idea to cast motion infilling as an inpainting problem and to train a convolutional de-noising autoencoder on image-like representations of motion sequences.
Towards End-to-end Video-based Eye-Tracking
Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors.
CoSE: Compositional Stroke Embeddings
We demonstrate qualitatively and quantitatively that our proposed approach is able to model the appearance of individual strokes, as well as the compositional structure of larger diagram drawings.
A Spatio-temporal Transformer for 3D Human Motion Prediction
We propose a novel Transformer-based architecture for the task of generative modelling of 3D human motion.
The DIDI dataset: Digital Ink Diagram data
We are releasing a dataset of diagram drawings with dynamic drawing information.
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios.
Structured Prediction Helps 3D Human Motion Modelling
This is implemented via a hierarchy of small-sized neural networks connected analogously to the kinematic chains in the human body as well as a joint-wise decomposition in the loss function.
Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks
Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction.
STCN: Stochastic Temporal Convolutional Networks
Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism.
Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time
To learn from sufficient data, we synthesize IMU data from motion capture datasets.
DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
Digital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text.
Guiding InfoGAN with Semi-Supervision
In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0. 22%, max.
Learning Human Motion Models for Long-term Predictions
Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists.
Learning Deep Temporal Representations for Brain Decoding
By employing the proposed temporal convolutional architecture with spatial pooling, raw input fMRI data is mapped to a non-linear, highly-expressive and low-dimensional feature space where the final classification is conducted.
