12 citations · 16 across the 12 of their papers we have counts for
12 papers
StdGEN++: A Comprehensive System for Semantic-Decomposed 3D Character Generation
Yuze He, Yanning Zhou, Wang Zhao +4
We present StdGEN++, a novel and comprehensive system for generating high-fidelity, semantically decomposed 3D characters from diverse inputs. Existing 3D generative methods often…
CHARM: Control-point-based 3D Anime Hairstyle Auto-Regressive Modeling
Yuze He, Yanning Zhou, Wang Zhao +6
We present CHARM, a novel parametric representation and generative framework for anime hairstyle modeling. While traditional hair modeling methods focus on realistic hair using str…
PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer
Jingwen Ye, Yuze He, Yanning Zhou +5
Shape primitive abstraction, which decomposes complex 3D shapes into simple geometric elements, plays a crucial role in human visual cognition and has broad applications in compute…
AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos
Yuze He, Wang Zhao, Shaohui Liu +4
We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation. By representing 3D planes as rectan…
StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
Yuze He, Yanning Zhou, Wang Zhao +5
We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gami…
PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction
Sheng Ye, Yuze He, Matthieu Lin +8
Neural implicit representations have revolutionized dense multi-view surface reconstruction, yet their performance significantly diminishes with sparse input views. A few pioneerin…
Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
Yuze He, Yushi Bai, Matthieu Lin +5
By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress. Many state-of-the-art approaches usually…
TBench: Benchmarking Current Progress in Text-to-3D Generation
Yuze He, Yushi Bai, Matthieu Lin +6
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3…
MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending
Yuze He, Peng Wang, Yubin Hu +4
This paper presents a flexible representation of neural radiance fields based on multi-plane images (MPI), for high-quality view synthesis of complex scenes. MPI with Normalized De…
O-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model
Yubin Hu, Sheng Ye, Wang Zhao +5
Occlusion is a common issue in 3D reconstruction from RGB-D videos, often blocking the complete reconstruction of objects and presenting an ongoing problem. In this paper, we propo…
Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos
Yubin Hu, Yuze He, Yanghao Li +4
Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive net…
Learning to compose 6-DoF omnidirectional videos using multi-sphere images
Jisheng Li, Yuze He, Yubin Hu +2
Omnidirectional video is an essential component of Virtual Reality. Although various methods have been proposed to generate content that can be viewed with six degrees of freedom (…