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Found 33 papers, 10 papers with code
RobustFusion: Human Volumetric Capture with Data-driven Visual Cues using a RGBD Camera
To enable robust tracking, we embrace both the initial model and the various visual cues into a novel performance capture scheme with hybrid motion optimization and semantic volumetric fusion, which can successfully capture challenging human motions under the monocular setting without pre-scanned detailed template and owns the reinitialization ability to recover from tracking failures and the disappear-reoccur scenarios.
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
In addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to enhance pedestrian perception in crowded scenes.
LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds
Quantitative and qualitative experiments show that our method outperforms the techniques based only on RGB images.
HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions
Monocular 3D motion capture (mocap) is beneficial to many applications.
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR
We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments.
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation
We present a novel neural refractive field(NeReF) to recover wavefront of transparent fluids by simultaneously estimating the surface position and normal of the fluid front.
NeuralHOFusion: Neural Volumetric Rendering under Human-object Interactions
In this paper, we propose NeuralHOFusion, a neural approach for volumetric human-object capture and rendering using sparse consumer RGBD sensors.
Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time
In this paper, we present a novel Fourier PlenOctree (FPO) technique to tackle efficient neural modeling and real-time rendering of dynamic scenes captured under the free-view video (FVV) setting.
NeuVV: Neural Volumetric Videos with Immersive Rendering and Editing
We further develop a hybrid neural-rasterization rendering framework to support consumer-level VR headsets so that the aforementioned volumetric video viewing and editing, for the first time, can be conducted immersively in virtual 3D space.
Artemis: Articulated Neural Pets with Appearance and Motion synthesis
Our ARTEMIS enables interactive motion control, real-time animation, and photo-realistic rendering of furry animals.
Video-driven Neural Physically-based Facial Asset for Production
We then model the facial expression, geometry and physically-based textures using separate VAEs with a global MLP-based expression mapping across the latent spaces, to preserve characteristics across respective attributes while maintaining explicit controls over geometry and texture.
NIMBLE: A Non-rigid Hand Model with Bones and Muscles
Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world.
HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs
The raw HumanNeRF can already produce reasonable rendering on sparse video inputs of unseen subjects and camera settings.
Towards Controllable and Photorealistic Region-wise Image Manipulation
Adaptive and flexible image editing is a desirable function of modern generative models.
iButter: Neural Interactive Bullet Time Generator for Human Free-viewpoint Rendering
To fill this gap, in this paper we propose a neural interactive bullet-time generator (iButter) for photo-realistic human free-viewpoint rendering from dense RGB streams, which enables flexible and interactive design for human bullet-time visual effects.
Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactions
In this paper, we propose a neural human performance capture and rendering system to generate both high-quality geometry and photo-realistic texture of both human and objects under challenging interaction scenarios in arbitrary novel views, from only sparse RGB streams.
Neural Relighting and Expression Transfer On Video Portraits
We present a neural relighting and expression transfer technique to transfer the facial expressions from a source performer to a portrait video of a target performer while enabling dynamic relighting.
Few-shot Neural Human Performance Rendering from Sparse RGBD Videos
To fill this gap, in this paper we propose a few-shot neural human rendering approach (FNHR) from only sparse RGBD inputs, which exploits the temporal and spatial redundancy to generate photo-realistic free-view output of human activities.
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction.
PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging
Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare.
RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream
To fill this gap, in this paper, we propose RobustFusion, a robust volumetric performance reconstruction system for human-object interaction scenarios using only a single RGBD sensor, which combines various data-driven visual and interaction cues to handle the complex interaction patterns and severe occlusions.
Editable Free-viewpoint Video Using a Layered Neural Representation
Such layered representation supports fully perception and realistic manipulation of the dynamic scene whilst still supporting a free viewing experience in a wide range.
SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos
In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
MirrorNeRF: One-shot Neural Portrait Radiance Field from Multi-mirror Catadioptric Imaging
In this paper, we propose MirrorNeRF - a one-shot neural portrait free-viewpoint rendering approach using a catadioptric imaging system with multiple sphere mirrors and a single high-resolution digital camera, which is the first to combine neural radiance field with catadioptric imaging so as to enable one-shot photo-realistic human portrait reconstruction and rendering, in a low-cost and casual capture setting.
Convolutional Neural Opacity Radiance Fields
In this paper, we propose a novel scheme to generate opacity radiance fields with a convolutional neural renderer for fuzzy objects, which is the first to combine both explicit opacity supervision and convolutional mechanism into the neural radiance field framework so as to enable high-quality appearance and global consistent alpha mattes generation in arbitrary novel views.
Neural Video Portrait Relighting in Real-time via Consistency Modeling
In this paper, we propose a neural approach for real-time, high-quality and coherent video portrait relighting, which jointly models the semantic, temporal and lighting consistency using a new dynamic OLAT dataset.
GNeRF: GAN-based Neural Radiance Field without Posed Camera
We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and even randomly initialized camera poses.
NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering using RGB Cameras
4D reconstruction and rendering of human activities is critical for immersive VR/AR experience. Recent advances still fail to recover fine geometry and texture results with the level of detail present in the input images from sparse multi-view RGB cameras.
ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References
We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner.
OccuSeg: Occupancy-aware 3D Instance Segmentation
3D instance segmentation, with a variety of applications in robotics and augmented reality, is in large demands these days.
Ranked #1 on
3D Instance Segmentation
on SceneNN
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.
Beyond SIFT using Binary features for Loop Closure Detection
The proposed system originates from our previous work Multi-Index hashing for Loop closure Detection (MILD), which employs Multi-Index Hashing (MIH)~\cite{greene1994multi} for Approximate Nearest Neighbor (ANN) search of binary features.
FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras
We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera.
