205 citations · 1.2k across the 52 of their papers we have counts for
56 papers
Ego4o: Egocentric Human Motion Capture and Understanding from Multi-Modal Input
Jian Wang, Rishabh Dabral, Diogo Luvizon +4
This work focuses on tracking and understanding human motion using consumer wearable devices, such as VR/AR headsets, smart glasses, cellphones, and smartwatches. These devices pro…
Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement
Jian Wang, Zhe Cao, Diogo Luvizon +5
In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion. This task presents significa…
Wonder3D: Single Image to 3D using Cross-Domain Diffusion
Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin +8
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 Samp…
DELIFFAS: Deformable Light Fields for Fast Avatar Synthesis
Youngjoong Kwon, Lingjie Liu, Henry Fuchs +2
Generating controllable and photorealistic digital human avatars is a long-standing and important problem in Vision and Graphics. Recent methods have shown great progress in terms…
State of the Art on Diffusion Models for Visual Computing
Ryan Po, Wang Yifan, Vladislav Golyanik +15
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
Yuan Liu, Cheng Lin, Zijiao Zeng +4
In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, rece…
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2
Diffusion models have demonstrated excellent potential for generating diverse images. However, their performance often suffers from slow generation due to iterative denoising. Know…
DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
Jingyu Zhuang, Chen Wang, Lingjie Liu +2
Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encodin…
NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images
Yuan Liu, Peng Wang, Cheng Lin +5
We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment.…
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Xingang Pan, Ayush Tewari, Thomas Leimkühler +3
Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existin…
General Neural Gauge Fields
Fangneng Zhan, Lingjie Liu, Adam Kortylewski +1
The recent advance of neural fields, such as neural radiance fields, has significantly pushed the boundary of scene representation learning. Aiming to boost the computation efficie…
Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction
Hansheng Chen, Jiatao Gu, Anpei Chen +4
3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a compreh…
Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images
Jiatao Gu, Qingzhe Gao, Shuangfei Zhai +3
Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulti…
F-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories
Peng Wang, Yuan Liu, Zhaoxi Chen +5
This paper presents a novel grid-based NeRF called F2-NeRF (Fast-Free-NeRF) for novel view synthesis, which enables arbitrary input camera trajectories and only costs a few minutes…
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
Jiatao Gu, Alex Trevithick, Kai-En Lin +4
Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the inpu…
Scene-aware Egocentric 3D Human Pose Estimation
Jian Wang, Lingjie Liu, Weipeng Xu +3
Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Ex…
NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction
Yiming Wang, Qin Han, Marc Habermann +3
Recent methods for neural surface representation and rendering, for example NeuS, have demonstrated the remarkably high-quality reconstruction of static scenes. However, the traini…
NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies
Xiaoxiao Long, Cheng Lin, Lingjie Liu +5
We present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies from 2D images via volume rendering. Recent advances in neural rendering based re…
HDHumans: A Hybrid Approach for High-fidelity Digital Humans
Marc Habermann, Lingjie Liu, Weipeng Xu +3
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 ca…
Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors
Yiming Wang, Qingzhe Gao, Libin Liu +3
We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation ca…
Progressively-connected Light Field Network for Efficient View Synthesis
Peng Wang, Yuan Liu, Guying Lin +4
This paper presents a Progressively-connected Light Field network (ProLiF), for the novel view synthesis of complex forward-facing scenes. ProLiF encodes a 4D light field, which al…
Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions
Weilin Wan, Lei Yang, Lingjie Liu +7
Understanding human intentions during interactions has been a long-lasting theme, that has applications in human-robot interaction, virtual reality and surveillance. In this study,…
Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data
Mengyu Chu, Lingjie Liu, Quan Zheng +4
High-fidelity reconstruction of fluids from sparse multiview RGB videos remains a formidable challenge due to the complexity of the underlying physics as well as complex occlusion…
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors
Jiepeng Wang, Peng Wang, Xiaoxiao Long +4
Reconstructing 3D indoor scenes from 2D images is an important task in many computer vision and graphics applications. A main challenge in this task is that large texture-less area…