110 citations · 387 across the 42 of their papers we have counts for
44 papers
StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset
Chaofan Huo, Ye Shi, Yuexin Ma +3
Modeling and capturing the 3D spatial arrangement of the human and the object is the key to perceiving 3D human-object interaction from monocular images. In this work, we propose t…
Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator
Hanzhuo Huang, Yufan Feng, Cheng Shi +3
Text-to-video is a rapidly growing research area that aims to generate a semantic, identical, and temporal coherence sequence of frames that accurately align with the input text pr…
HACK: Learning a Parametric Head and Neck Model for High-fidelity Animation
Longwen Zhang, Zijun Zhao, Xinzhou Cong +7
Significant advancements have been made in developing parametric models for digital humans, with various approaches concentrating on parts such as the human body, hand, or face. Ne…
InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions
Han Liang, Wenqian Zhang, Wenxuan Li +2
We have recently seen tremendous progress in diffusion advances for generating realistic human motions. Yet, they largely disregard the multi-human interactions. In this paper, we…
Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos
Liao Wang, Qiang Hu, Qihan He +5
The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has inspired numerous attempts on dynamic scenes. Current techniques that util…
DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance
Longwen Zhang, Qiwei Qiu, Hongyang Lin +7
Emerging Metaverse applications demand accessible, accurate, and easy-to-use tools for 3D digital human creations in order to depict different cultures and societies as if in the p…
NEPHELE: A Neural Platform for Highly Realistic Cloud Radiance Rendering
Haimin Luo, Siyuan Zhang, Fuqiang Zhao +10
We have recently seen tremendous progress in neural rendering (NR) advances, i.e., NeRF, for photo-real free-view synthesis. Yet, as a local technique based on a single computer/GP…
IKOL: Inverse kinematics optimization layer for 3D human pose and shape estimation via Gauss-Newton differentiation
Juze Zhang, Ye Shi, Yuexin Ma +3
This paper presents an inverse kinematic optimization layer (IKOL) for 3D human pose and shape estimation that leverages the strength of both optimization- and regression-based met…
NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions
Juze Zhang, Haimin Luo, Hongdi Yang +6
Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions,…
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR
Peishan Cong, Yiteng Xu, Yiming Ren +5
Depth estimation is usually ill-posed and ambiguous for monocular camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate depth information in long-range sce…
Gait Recognition in Large-scale Free Environment via Single LiDAR
Xiao Han, Yiming Ren, Peishan Cong +4
Human gait recognition is crucial in multimedia, enabling identification through walking patterns without direct interaction, enhancing the integration across various media forms i…
Human Performance Modeling and Rendering via Neural Animated Mesh
Fuqiang Zhao, Yuheng Jiang, Kaixin Yao +8
We have recently seen tremendous progress in the neural advances for photo-real human modeling and rendering. However, it's still challenging to integrate them into an existing mes…
NARRATE: A Normal Assisted Free-View Portrait Stylizer
Youjia Wang, Teng Xu, Yiwen Wu +4
In this work, we propose NARRATE, a novel pipeline that enables simultaneously editing portrait lighting and perspective in a photorealistic manner. As a hybrid neural-physical fac…
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation
Juze Zhang, Jingya Wang, Ye Shi +3
Inter-person occlusion and depth ambiguity make estimating the 3D poses of monocular multiple persons as camera-centric coordinates a challenging problem. Typical top-down framewor…
LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors
Yiming Ren, Chengfeng Zhao, Yannan He +5
We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only usin…
LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds
Jialian Li, Jingyi Zhang, Zhiyong Wang +6
Existing motion capture datasets are largely short-range and cannot yet fit the need of long-range applications. We propose LiDARHuman26M, a new human motion capture dataset captur…
NeuralHOFusion: Neural Volumetric Rendering under Human-object Interactions
Yuheng Jiang, Suyi Jiang, Guoxing Sun +5
4D modeling of human-object interactions is critical for numerous applications. However, efficient volumetric capture and rendering of complex interaction scenarios, especially fro…
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation
Ziyu Wang, Wei Yang, Junming Cao +3
Existing neural reconstruction schemes such as Neural Radiance Field (NeRF) are largely focused on modeling opaque objects. We present a novel neural refractive field(NeReF) to rec…
HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions
Han Liang, Yannan He, Chengfeng Zhao +4
Monocular 3D motion capture (mocap) is beneficial to many applications. The use of a single camera, however, often fails to handle occlusions of different body parts and hence it i…
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR
Yudi Dai, Yitai Lin, Chenglu Wen +5
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…
Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time
Liao Wang, Jiakai Zhang, Xinhang Liu +6
Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly on modeling static objects captured under multi-view settings where real-time rendering can…
NeuVV: Neural Volumetric Videos with Immersive Rendering and Editing
Jiakai Zhang, Liao Wang, Xinhang Liu +7
Some of the most exciting experiences that Metaverse promises to offer, for instance, live interactions with virtual characters in virtual environments, require real-time photo-rea…
Artemis: Articulated Neural Pets with Appearance and Motion synthesis
Haimin Luo, Teng Xu, Yuheng Jiang +6
We, humans, are entering into a virtual era and indeed want to bring animals to the virtual world as well for companion. Yet, computer-generated (CGI) furry animals are limited by…
Video-driven Neural Physically-based Facial Asset for Production
Longwen Zhang, Chuxiao Zeng, Qixuan Zhang +5
Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion ca…