131 citations · 350 across the 16 of their papers we have counts for
16 papers
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…
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…
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…
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
Wenhai Wang, Zhe Chen, Xiaokang Chen +8
Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endo…
Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Prediction
Xiaokang Chen, Yajie Xing, Gang Zeng
Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. In this paper, we propose a real-time semantic scene co…
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…
DETR: Decoder Distillation for Detection Transformer
Xiaokang Chen, Jiahui Chen, Yan Liu +1
While various knowledge distillation (KD) methods in CNN-based detectors show their effectiveness in improving small students, the baselines and recipes for DETR-based detectors ar…
Conditional DETR V2: Efficient Detection Transformer with Box Queries
Xiaokang Chen, Fangyun Wei, Gang Zeng +1
In this paper, we are interested in Detection Transformer (DETR), an end-to-end object detection approach based on a transformer encoder-decoder architecture without hand-crafted p…
Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment
Qiang Chen, Xiaokang Chen, Jian Wang +7
Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth object to one prediction, for end-to-end detection without NMS post-processing. It is known…
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…
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation
Min Zhong, Xinghao Chen, Xiaokang Chen +2
This paper studies the 3D instance segmentation problem, which has a variety of real-world applications such as robotics and augmented reality. Since the surroundings of 3D objects…
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…
Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
Xiaokang Chen, Kwan-Yee Lin, Jingbo Wang +4
Depth information has proven to be a useful cue in the semantic segmentation of RGB-D images for providing a geometric counterpart to the RGB representation. Most existing works si…
3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior
Xiaokang Chen, Kwan-Yee Lin, Chen Qian +2
The goal of the Semantic Scene Completion (SSC) task is to simultaneously predict a completed 3D voxel representation of volumetric occupancy and semantic labels of objects in the…