175 citations · 434 across the 21 of their papers we have counts for
22 papers
CleanMDM: Clean Motion Diffusion Model for Multimodal Motion Cleanup
Zhe Li, Shicheng Wang, Bowen Cai +1
Motion capture data is rarely directly usable, as they typically exhibit missing segments, jitter, drift and contact artifacts. Traditionally, corrupted motions are cleaned by anim…
TOPOS: High-Fidelity and Efficient Industry-Grade 3D Head Generation
Bojun Xiong, Zoubin Bi, Xinghui Peng +6
High-fidelity 3D head generation plays a crucial role in the film, animation and video game industries. In industrial pipelines, studios typically enforce a fixed reference topolog…
High-Fidelity Single-Image Head Modeling with Industry-Grade Topology
Yunmu Wang, Zoubin Bi, Bowen Cai +6
We present a single-image head mesh reconstruction framework that addresses the longstanding challenge of simultaneously preserving facial identity and producing industry-grade top…
PerformRecast: Expression and Head Pose Disentanglement for Portrait Video Editing
Jiadong Liang, Bojun Xiong, Jie Tian +4
This paper primarily investigates the task of expression-only portrait video performance editing based on a driving video, which plays a crucial role in animation and film industri…
Skeleton2Stage: Reward-Guided Fine-Tuning for Physically Plausible Dance Generation
Jidong Jia, Youjian Zhang, Huan Fu +1
Despite advances in dance generation, most methods are trained in the skeletal domain and ignore mesh-level physical constraints. As a result, motions that look plausible as joint…
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering
Dongting Hu, Zhenkai Zhang, Tingbo Hou +3
The rendering scheme in neural radiance field (NeRF) is effective in rendering a pixel by casting a ray into the scene. However, NeRF yields blurred rendering results when the trai…
NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction
Bowen Cai, Jinchi Huang, Rongfei Jia +2
This paper studies implicit surface reconstruction leveraging differentiable ray casting. Previous works such as IDR and NeuS overlook the spatial context in 3D space when predicti…
3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue
Bowen Cai, Yujie Li, Yuqin Liang +4
This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty,…
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
Jian Zhang, Yuanqing Zhang, Huan Fu +6
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images. A main limitation of conventional NeRFs is that the…
Modeling Indirect Illumination for Inverse Rendering
Yuanqing Zhang, Jiaming Sun, Xingyi He +3
Recent advances in implicit neural representations and differentiable rendering make it possible to simultaneously recover the geometry and materials of an object from multi-view R…
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Bowen Cai, Huan Fu, Rongfei Jia +3
Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods…
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Huan Fu, Bowen Cai, Lin Gao +7
We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a new, large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally des…
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Huan Fu, Shunming Li, Rongfei Jia +3
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shap…
3D-FUTURE: 3D Furniture shape with TextURE
Huan Fu, Rongfei Jia, Lin Gao +4
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textur…
Adaptive Context-Aware Multi-Modal Network for Depth Completion
Shanshan Zhao, Mingming Gong, Huan Fu +1
Depth completion aims to recover a dense depth map from the sparse depth data and the corresponding single RGB image. The observed pixels provide the significant guidance for the r…
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Yipeng Mou, Mingming Gong, Huan Fu +3
Majority of state-of-the-art monocular depth estimation methods are supervised learning approaches. The success of such approaches heavily depends on the high-quality depth labels…
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Shanshan Zhao, Huan Fu, Mingming Gong +1
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures. Since the groundtruth depth labels are hard to obtain, recent methods try to…
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Huan Fu, Mingming Gong, Chaohui Wang +3
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is…
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
Huan Fu, Mingming Gong, Chaohui Wang +1
Scene parsing is an indispensable component in understanding the semantics within a scene. Traditional methods rely on handcrafted local features and probabilistic graphical models…
Deep Ordinal Regression Network for Monocular Depth Estimation
Huan Fu, Mingming Gong, Chaohui Wang +2
Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring…
A Compromise Principle in Deep Monocular Depth Estimation
Huan Fu, Mingming Gong, Chaohui Wang +1
Monocular depth estimation, which plays a key role in understanding 3D scene geometry, is fundamentally an ill-posed problem. Existing methods based on deep convolutional neural ne…
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
Kede Ma, Huan Fu, Tongliang Liu +2
The human visual system excels at detecting local blur of visual images, but the underlying mechanism is not well understood. Traditional views of blur such as reduction in energy…