Search Results for author:
Found 8 papers, 5 papers with code
Exploring Text-to-Motion Generation with Human Preference
This paper presents an exploration of preference learning in text-to-motion generation.
Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress.
Augmenting Unsupervised Reinforcement Learning with Self-Reference
Our approach achieves state-of-the-art results in terms of Interquartile Mean (IQM) performance and Optimality Gap reduction on the Unsupervised Reinforcement Learning Benchmark for model-free methods, recording an 86% IQM and a 16% Optimality Gap.
T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF.
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors.
ExpeL: LLM Agents Are Experiential Learners
The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs.
O$^2$-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model
In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting model, to reconstruct complete surfaces for the hidden parts of objects.
DETR for Crowd Pedestrian Detection
Furthermore, the bipartite match of ED harms the training efficiency due to the large ground truth number in crowd scenes.
