88 citations · 423 across the 74 of their papers we have counts for
20 papers · 1 filter
InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds
Tianjian Jiang, Xu Chen, Jie Song +1
In this paper, we take a significant step towards real-world applicability of monocular neural avatar reconstruction by contributing InstantAvatar, a system that can reconstruct hu…
HARP: Personalized Hand Reconstruction from a Monocular RGB Video
Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges +1
We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstr…
PointAvatar: Deformable Point-based Head Avatars from Videos
Yufeng Zheng, Wang Yifan, Gordon Wetzstein +2
The ability to create realistic, animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment. Cur…
HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics
Artur Grigorev, Bernhard Thomaszewski, Michael J. Black +1
We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics. Wher…
GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields
Alessandro Ruzzi, Xiangwei Shi, Xi Wang +5
We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Inst…
Fast-SNARF: A Fast Deformer for Articulated Neural Fields
Xu Chen, Tianjian Jiang, Jie Song +4
Neural fields have revolutionized the area of 3D reconstruction and novel view synthesis of rigid scenes. A key challenge in making such methods applicable to articulated objects,…
DINER: Depth-aware Image-based NEural Radiance fields
Malte Prinzler, Otmar Hilliges, Justus Thies
We present Depth-aware Image-based NEural Radiance fields (DINER). Given a sparse set of RGB input views, we predict depth and feature maps to guide the reconstruction of a volumet…
Utilizing Synthetic Data in Supervised Learning for Robust 5-DoF Magnetic Marker Localization
Mengfan Wu, Thomas Langerak, Otmar Hilliges +1
Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems.…
Computational Design of Active Kinesthetic Garments
Velko Vechev, Ronan Hinchet, Stelian Coros +2
Garments with the ability to provide kinesthetic force-feedback on-demand can augment human capabilities in a non-obtrusive way, enabling numerous applications in VR haptics, motio…
Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors
Xi Wang, Gen Li, Yen-Ling Kuo +3
We present a method for inferring diverse 3D models of human-object interactions from images. Reasoning about how humans interact with objects in complex scenes from a single 2D im…
MARLUI: Multi-Agent Reinforcement Learning for Adaptive UIs
Thomas Langerak, Sammy Christen, Mert Albaba +2
Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful…
TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning
Andrea Ziani, Zicong Fan, Muhammed Kocabas +2
We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction. Unlike previous time-contrastive methods for h…
EyeNeRF: A Hybrid Representation for Photorealistic Synthesis, Animation and Relighting of Human Eyes
Gengyan Li, Abhimitra Meka, Franziska Müller +3
A unique challenge in creating high-quality animatable and relightable 3D avatars of people is modeling human eyes. The challenge of synthesizing eyes is multifold as it requires 1…
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning
Marco Bagatella, Sammy Christen, Otmar Hilliges
Efficient exploration is a crucial challenge in deep reinforcement learning. Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently a…
Computational Design of Kinesthetic Garments
Velko Vechev, Juan Zarate, Bernhard Thomaszewski +1
Kinesthetic garments provide physical feedback on body posture and motion through tailored distributions of reinforced material. Their ability to selectively stiffen a garment's re…
PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence
Zijian Dong, Chen Guo, Jie Song +3
We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtu…
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
Zicong Fan, Omid Taheri, Dimitrios Tzionas +4
Humans intuitively understand that inanimate objects do not move by themselves, but that state changes are typically caused by human manipulation (e.g., the opening of a book). Thi…
LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space
Emre Aksan, Shugao Ma, Akin Caliskan +5
Neural face avatars that are trained from multi-view data captured in camera domes can produce photo-realistic 3D reconstructions. However, at inference time, they must be driven b…
In the Arms of a Robot: Designing Autonomous Hugging Robots with Intra-Hug Gestures
Alexis E. Block, Hasti Seifi, Otmar Hilliges +2
Hugs are complex affective interactions that often include gestures like squeezes. We present six new guidelines for designing interactive hugging robots, which we validate through…
gDNA: Towards Generative Detailed Neural Avatars
Xu Chen, Tianjian Jiang, Jie Song +4
To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is challenging…