Search Results for author:
Found 9 papers, 4 papers with code
ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model
Advancements in 3D scene reconstruction have transformed 2D images from the real world into 3D models, producing realistic 3D results from hundreds of input photos.
DreamCinema: Cinematic Transfer with Free Camera and 3D Character
We are living in a flourishing era of digital media, where everyone has the potential to become a personal filmmaker.
AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment
Conversational Query Reformulation (CQR) has significantly advanced in addressing the challenges of conversational search, particularly those stemming from the latent user intent and the need for historical context.
LGTM: Local-to-Global Text-Driven Human Motion Diffusion Model
In this paper, we introduce LGTM, a novel Local-to-Global pipeline for Text-to-Motion generation.
InterFusion: Text-Driven Generation of 3D Human-Object Interaction
In this study, we tackle the complex task of generating 3D human-object interactions (HOI) from textual descriptions in a zero-shot text-to-3D manner.
Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language.
MirageRoom: 3D Scene Segmentation with 2D Pre-trained Models by Mirage Projection
Nowadays leveraging 2D images and pre-trained models to guide 3D point cloud feature representation has shown a remarkable potential to boost the performance of 3D fundamental models.
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network
It results in a serious issue of lagged gradient, making the learned attack at the current step ineffective due to the architecture changes afterward.
A Dynamic Keypoints Selection Network for 6DoF Pose Estimation
Given an RGBD image, our network is trained to predict pixel category and the translation to edge points and center points.
