30 citations · 95 across the 20 of their papers we have counts for
21 papers
CABLE: Cloud-Assisted Bandwidth-efficient LMM-based Encoding for V2X Systems
Haohua Que, Zhipeng Bao, Qianyi Wu +1
Cloud-hosted large multimodal models (LMMs) can provide strong open-vocabulary perception for Vehicle-to-Everything systems, but naively transmitting full-resolution frames from ed…
How Far Can We Compress Instant-NGP-Based NeRF?
Yihang Chen, Qianyi Wu, Mehrtash Harandi +1
In recent years, Neural Radiance Field (NeRF) has demonstrated remarkable capabilities in representing 3D scenes. To expedite the rendering process, learnable explicit representati…
GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal
Yuxin Wang, Qianyi Wu, Guofeng Zhang +1
This paper tackles the intricate challenge of object removal to update the radiance field using the 3D Gaussian Splatting. The main challenges of this task lie in the preservation…
Taming Stable Diffusion for Text to 360° Panorama Image Generation
Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella +4
Generative models, e.g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts. Yet, the generation of 360-degree panorama images from text remains…
ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition
Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham +1
3D decomposition/segmentation still remains a challenge as large-scale 3D annotated data is not readily available. Contemporary approaches typically leverage 2D machine-generated s…
HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression
Yihang Chen, Qianyi Wu, Weiyao Lin +2
3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity. However, the substantial Gaussians an…
ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces
Qianyi Wu, Kaisiyuan Wang, Kejie Li +2
In recent years, neural implicit surface reconstruction has emerged as a popular paradigm for multi-view 3D reconstruction. Unlike traditional multi-view stereo approaches, the neu…
Explicit Correspondence Matching for Generalizable Neural Radiance Fields
Yuedong Chen, Haofei Xu, Qianyi Wu +3
We present a new generalizable NeRF method that is able to directly generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The key to…
Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation
Yasheng Sun, Qianyi Wu, Hang Zhou +6
Creating the photo-realistic version of people sketched portraits is useful to various entertainment purposes. Existing studies only generate portraits in the 2D plane with fixed v…
Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers
Yasheng Sun, Hang Zhou, Kaisiyuan Wang +7
Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. However, most of them deform or generate the whole facia…
Audio-Driven Co-Speech Gesture Video Generation
Xian Liu, Qianyi Wu, Hang Zhou +4
Co-speech gesture is crucial for human-machine interaction and digital entertainment. While previous works mostly map speech audio to human skeletons (e.g., 2D keypoints), directly…
Object-Compositional Neural Implicit Surfaces
Qianyi Wu, Xian Liu, Yuedong Chen +4
The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on…
EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model
Xinya Ji, Hang Zhou, Kaisiyuan Wang +4
Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In t…
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing
Yanbo Xu, Yueqin Yin, Liming Jiang +5
Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have…
Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation
Xian Liu, Qianyi Wu, Hang Zhou +7
Generating speech-consistent body and gesture movements is a long-standing problem in virtual avatar creation. Previous studies often synthesize pose movement in a holistic manner,…
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
Yuedong Chen, Qianyi Wu, Chuanxia Zheng +2
Image translation and manipulation have gain increasing attention along with the rapid development of deep generative models. Although existing approaches have brought impressive r…
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
Yicheng Wu, Zhonghua Wu, Qianyi Wu +2
Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive e…
Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation
Xian Liu, Yinghao Xu, Qianyi Wu +3
Animating high-fidelity video portrait with speech audio is crucial for virtual reality and digital entertainment. While most previous studies rely on accurate explicit structural…
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
Hao Zhu, Chaoyou Fu, Qianyi Wu +3
Recent studies have shown that the performance of forgery detection can be improved with diverse and challenging Deepfakes datasets. However, due to the lack of Deepfakes datasets…
Disentangled Representation Learning for 3D Face Shape
Zi-Hang Jiang, Qianyi Wu, Keyu Chen +1
In this paper, we present a novel strategy to design disentangled 3D face shape representation. Specifically, a given 3D face shape is decomposed into identity part and expression…
Alive Caricature from 2D to 3D
Qianyi Wu, Juyong Zhang, Yu-Kun Lai +2
Caricature is an art form that expresses subjects in abstract, simple and exaggerated view. While many caricatures are 2D images, this paper presents an algorithm for creating expr…