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Found 33 papers, 5 papers with code
MetaCap: Meta-learning Priors from Multi-View Imagery for Sparse-view Human Performance Capture and Rendering
Our key idea is to meta-learn the radiance field weights solely from potentially sparse multi-view videos, which can serve as a prior when fine-tuning them on sparse imagery depicting the human.
ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis
Gestures play a key role in human communication.
Relightable Neural Actor with Intrinsic Decomposition and Pose Control
Creating a digital human avatar that is relightable, drivable, and photorealistic is a challenging and important problem in Vision and Graphics.
Holoported Characters: Real-time Free-viewpoint Rendering of Humans from Sparse RGB Cameras
At inference, our method only requires four camera views of the moving actor and the respective 3D skeletal pose.
ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering
Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics.
TriHuman : A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis
Creating controllable, photorealistic, and geometrically detailed digital doubles of real humans solely from video data is a key challenge in Computer Graphics and Vision, especially when real-time performance is required.
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models
The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions.
Wonder3D: Single Image to 3D using Cross-Domain Diffusion
In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry.
Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering
We further conduct an extensive comparative study of different priors on illumination used in previous work on inverse rendering.
NeuralClothSim: Neural Deformation Fields Meet the Kirchhoff-Love Thin Shell Theory
Cloth simulation is an extensively studied problem, with a plethora of solutions available in computer graphics literature.
ROAM: Robust and Object-Aware Motion Generation Using Neural Pose Descriptors
Given an unseen object and a reference pose-object pair, we optimise for the object-aware pose that is closest in the feature space to the reference pose.
VINECS: Video-based Neural Character Skinning
However, the former methods typically predict solely static skinning weights, which perform poorly for highly articulated poses, and the latter ones either require dense 3D character scans in different poses or cannot generate an explicit mesh with vertex correspondence over time.
EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors
We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera.
Scene-Aware 3D Multi-Human Motion Capture from a Single Camera
In this work, we consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera.
NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction
Recent methods for neural surface representation and rendering, for example NeuS, have demonstrated the remarkably high-quality reconstruction of static scenes.
State of the Art in Dense Monocular Non-Rigid 3D Reconstruction
3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics.
HDHumans: A Hybrid Approach for High-fidelity Digital Humans
Photo-real digital human avatars are of enormous importance in graphics, as they enable immersive communication over the globe, improve gaming and entertainment experiences, and can be particularly beneficial for AR and VR settings.
HiFECap: Monocular High-Fidelity and Expressive Capture of Human Performances
Furthermore, we show that HiFECap outperforms the state-of-the-art human performance capture approaches qualitatively and quantitatively while for the first time capturing all aspects of the human.
Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination
Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics.
Unbiased 4D: Monocular 4D Reconstruction with a Neural Deformation Model
Capturing general deforming scenes from monocular RGB video is crucial for many computer graphics and vision applications.
A Deeper Look into DeepCap
Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.
NRST: Non-rigid Surface Tracking from Monocular Video
Our texture term exploits the orientation information in the micro-structures of the objects, e. g., the yarn patterns of fabrics.
Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control
To address this problem, we utilize a coarse body model as the proxy to unwarp the surrounding 3D space into a canonical pose.
Real-time Deep Dynamic Characters
We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery.
Efficient and Differentiable Shadow Computation for Inverse Problems
Differentiable rendering has received increasing interest for image-based inverse problems.
Monocular Real-time Full Body Capture with Inter-part Correlations
We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image.
Ranked #11 on
3D Hand Pose Estimation
on FreiHAND
Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture
Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera.
Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data
We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy.
DeepCap: Monocular Human Performance Capture Using Weak Supervision
Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.
Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation
In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space.
EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera
The high frame rate is a critical requirement for capturing fast human motions.
LiveCap: Real-time Human Performance Capture from Monocular Video
Our method is the first real-time monocular approach for full-body performance capture.
Neural Rendering and Reenactment of Human Actor Videos
In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person.
