75 citations · 259 across the 22 of their papers we have counts for
23 papers
SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation
Jun Luo, Jiaxiang Tang, Ruijie Lu +1
Text-to-3D scene generation from natural language is highly desirable for digital content creation. However, existing methods are largely domain-restricted or reliant on predefined…
DreamArt: Generating Interactable Articulated Objects from a Single Image
Ruijie Lu, Yu Liu, Jiaxiang Tang +6
Generating articulated objects, such as laptops and microwaves, is a crucial yet challenging task with extensive applications in Embodied AI and AR/VR. Current image-to-3D methods…
Efficient Part-level 3D Object Generation via Dual Volume Packing
Jiaxiang Tang, Ruijie Lu, Zhaoshuo Li +7
Recent progress in 3D object generation has greatly improved both the quality and efficiency. However, most existing methods generate a single mesh with all parts fused together, w…
TACO: Taming Diffusion for in-the-wild Video Amodal Completion
Ruijie Lu, Yixin Chen, Yu Liu +5
Humans can infer complete shapes and appearances of objects from limited visual cues, relying on extensive prior knowledge of the physical world. However, completing partially obse…
EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
Jiaxiang Tang, Zhaoshuo Li, Zekun Hao +4
Current auto-regressive mesh generation methods suffer from issues such as incompleteness, insufficient detail, and poor generalization. In this paper, we propose an Auto-regressiv…
3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong +10
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative mo…
InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpainting
Jiaxiang Tang, Ruijie Lu, Xiaokang Chen +3
Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models. Existing methods primarily adopt a combination of…
3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors
Fangzhou Hong, Jiaxiang Tang, Ziang Cao +8
We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage…
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation
Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen +3
3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution i…
DreamGaussian4D: Generative 4D Gaussian Splatting
Jiawei Ren, Liang Pan, Jiaxiang Tang +4
4D content generation has achieved remarkable progress recently. However, existing methods suffer from long optimization times, a lack of motion controllability, and a low quality…
HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting
Xian Liu, Xiaohang Zhan, Jiaxiang Tang +5
Realistic 3D human generation from text prompts is a desirable yet challenging task. Existing methods optimize 3D representations like mesh or neural fields via score distillation…
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
Jiaxiang Tang, Jiawei Ren, Hang Zhou +2
Recent advances in 3D content creation mostly leverage optimization-based 3D generation via score distillation sampling (SDS). Though promising results have been exhibited, these m…
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
Yangyi Huang, Hongwei Yi, Yuliang Xiu +4
Despite recent research advancements in reconstructing clothed humans from a single image, accurately restoring the "unseen regions" with high-level details remains an unsolved cha…
Interactive Segment Anything NeRF with Feature Imitation
Xiaokang Chen, Jiaxiang Tang, Diwen Wan +2
This paper investigates the potential of enhancing Neural Radiance Fields (NeRF) with semantics to expand their applications. Although NeRF has been proven useful in real-world app…
Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement
Jiaxiang Tang, Hang Zhou, Xiaokang Chen +4
Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly f…
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
Jiaxiang Tang, Kaisiyuan Wang, Hang Zhou +6
While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their pote…
Secure Embedding Aggregation for Federated Representation Learning
Jiaxiang Tang, Jinbao Zhu, Songze Li +1
We consider a federated representation learning framework, where with the assistance of a central server, a group of distributed clients train collaboratively over their privat…
Compressible-composable NeRF via Rank-residual Decomposition
Jiaxiang Tang, Xiaokang Chen, Jingbo Wang +1
Neural Radiance Field (NeRF) has emerged as a compelling method to represent 3D objects and scenes for photo-realistic rendering. However, its implicit representation causes diffic…
Point Scene Understanding via Disentangled Instance Mesh Reconstruction
Jiaxiang Tang, Xiaokang Chen, Jingbo Wang +1
Semantic scene reconstruction from point cloud is an essential and challenging task for 3D scene understanding. This task requires not only to recognize each instance in the scene,…
Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective
Xiaokang Chen, Jiaxiang Tang, Jingbo Wang +1
We revisit Semantic Scene Completion (SSC), a useful task to predict the semantic and occupancy representation of 3D scenes, in this paper. A number of methods for this task are al…
Joint Implicit Image Function for Guided Depth Super-Resolution
Jiaxiang Tang, Xiaokang Chen, Gang Zeng
Guided depth super-resolution is a practical task where a low-resolution and noisy input depth map is restored to a high-resolution version, with the help of a high-resolution RGB…
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
Jiaxiang Tang, Wei Hu, Xiang Gao +1
Graph Convolutional Neural Networks (GCNNs) extend classical CNNs to graph data domain, such as brain networks, social networks and 3D point clouds. It is critical to identify an a…
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
Xiang Gao, Wei Hu, Jiaxiang Tang +2
With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skel…