Search Results for author:
Found 10 papers, 5 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.
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and customizing their behaviors.
Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability.
Ranked #1 on
Single-View 3D Reconstruction
on GSO
DreamReward: Text-to-3D Generation with Human Preference
Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL), a direct tuning algorithm to optimize the multi-view diffusion models with a redefined scorer.
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.
Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior
Instead of retraining a costly viewpoint-aware model, we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement.
Discovering Dynamic Causal Space for DAG Structure Learning
Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.
Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention
In this paper, we aim to learn a semantic radiance field from multiple scenes that is accurate, efficient and generalizable.
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.
