Search Results for author:
Found 16 papers, 8 papers with code
Motion Capture from Internet Videos
Therefore, we propose to capture human motion by jointly analyzing these Internet videos instead of using single videos separately.
Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans
To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated.
Reconstructing 3D Human Pose by Watching Humans in the Mirror
In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror.
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies
Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model.
Efficient Neural Radiance Fields for Interactive Free-viewpoint Video
We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos.
Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos
Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images.
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes
The key challenge of this problem is to efficiently match 2D observations across multiple views.
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3D Multi-Person Pose Estimation
on Shelf
Reconstructing Hand-Held Objects from Monocular Video
The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker.
Learning Analytical Posterior Probability for Human Mesh Recovery
Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks.
iVS-Net: Learning Human View Synthesis from Internet Videos
However, these methods usually rely on limited multi-view images typically collected in the studio or commercial high-quality 3D scans for training, which heavily prohibits their generalization capability for in-the-wild images.
Representing Volumetric Videos as Dynamic MLP Maps
This paper introduces a novel representation of volumetric videos for real-time view synthesis of dynamic scenes.
Learning Human Mesh Recovery in 3D Scenes
We present a novel method for recovering the absolute pose and shape of a human in a pre-scanned scene given a single image.
Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
We extensively evaluate our approach on various scenes and show that our approach achieves spatially and temporally consistent editing results.
EasyVolcap: Accelerating Neural Volumetric Video Research
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations.
AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model
In this paper, we introduce AniDress, a novel method for generating animatable human avatars in loose clothes using very sparse multi-view videos (4-8 in our setting).
Reconstructing Close Human Interactions from Multiple Views
This paper addresses the challenging task of reconstructing the poses of multiple individuals engaged in close interactions, captured by multiple calibrated cameras.
