Search Results for author:
Found 13 papers, 6 papers with code
VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models.
NICP: Neural ICP for 3D Human Registration at Scale
NSR achieves the state of the art over public benchmarks, and the release of its code and checkpoints will provide the community with a powerful tool useful for many downstream tasks like dataset alignments, cleaning, or asset animation.
Structured 3D Features for Reconstructing Controllable Avatars
We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface.
Learned Vertex Descent: A New Direction for 3D Human Model Fitting
An exhaustive evaluation demonstrates that our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art.
LISA: Learning Implicit Shape and Appearance of Hands
This paper proposes a do-it-all neural model of human hands, named LISA.
Enhancing Egocentric 3D Pose Estimation with Third Person Views
In this paper, we propose a novel approach to enhance the 3D body pose estimation of a person computed from videos captured from a single wearable camera.
SMPLicit: Topology-aware Generative Model for Clothed People
In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry.
Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
D-NeRF: Neural Radiance Fields for Dynamic Scenes
In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions from a \emph{single} camera moving around the scene.
PI-Net: Pose Interacting Network for Multi-Person Monocular 3D Pose Estimation
Our pose interacting network, or PI-Net, inputs the initial pose estimates of a variable number of interactees into a recurrent architecture used to refine the pose of the person-of-interest.
3D Multi-Person Pose Estimation (root-relative)
3D Pose Estimation
GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes
To this end, we introduce a generative model that jointly reasons in all these levels and 1) regresses the 3D shape and pose of the objects in the scene; 2) estimates the grasp types; and 3) refines the 51-DoF of a 3D hand model that minimize a graspability loss.
Context-aware Human Motion Prediction
The problem of predicting human motion given a sequence of past observations is at the core of many applications in robotics and computer vision.
Pose Estimation for Objects with Rotational Symmetry
In particular, our aim is to infer poses for objects not seen at training time, but for which their 3D CAD models are available at test time.
