5 citations · 11 across the 15 of their papers we have counts for
15 papers
LHM++: An Efficient Large Human Reconstruction Model for Pose-free Images to 3D
Lingteng Qiu, Peihao Li, Heyuan Li +9
Reconstructing animatable 3D humans from casually captured images of articulated subjects without camera or pose information is highly practical but remains challenging due to view…
LAM: Large Avatar Model for One-shot Animatable Gaussian Head
Yisheng He, Xiaodong Gu, Xiaodan Ye +6
We present LAM, an innovative Large Avatar Model for animatable Gaussian head reconstruction from a single image. Unlike previous methods that require extensive training on capture…
LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds
Lingteng Qiu, Xiaodong Gu, Peihao Li +8
Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation. Recent advances in 3D huma…
MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation
Shenhao Zhu, Lingteng Qiu, Xiaodong Gu +11
Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D metho…
AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction
Lingteng Qiu, Shenhao Zhu, Qi Zuo +9
Generating animatable human avatars from a single image is essential for various digital human modeling applications. Existing 3D reconstruction methods often struggle to capture f…
MVImgNet2.0: A Larger-scale Dataset of Multi-view Images
Xiaoguang Han, Yushuang Wu, Luyue Shi +7
MVImgNet is a large-scale dataset that contains multi-view images of ~220k real-world objects in 238 classes. As a counterpart of ImageNet, it introduces 3D visual signals via mult…
LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning
Zhe Li, Weihao Yuan, Yisheng He +7
Language plays a vital role in the realm of human motion. Existing methods have largely depended on CLIP text embeddings for motion generation, yet they fall short in effectively a…
Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction
Rui Peng, Shihe Shen, Kaiqiang Xiong +4
Reconstructing the high-fidelity surface from multi-view images, especially sparse images, is a critical and practical task that has attracted widespread attention in recent years.…
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling
Weihao Yuan, Weichao Shen, Yisheng He +5
Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors. Previous methods usually quantiz…
HIVE: HIerarchical Volume Encoding for Neural Implicit Surface Reconstruction
Xiaodong Gu, Weihao Yuan, Heng Li +2
Neural implicit surface reconstruction has become a new trend in reconstructing a detailed 3D shape from images. In previous methods, however, the 3D scene is only encoded by the M…
StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
Chongjie Ye, Lingteng Qiu, Xiaodong Gu +6
This work addresses the challenge of high-quality surface normal estimation from monocular colored inputs (i.e., images and videos), a field which has recently been revolutionized…
GenS: Generalizable Neural Surface Reconstruction from Multi-View Images
Rui Peng, Xiaodong Gu, Luyang Tang +3
Combining the signed distance function (SDF) and differentiable volume rendering has emerged as a powerful paradigm for surface reconstruction from multi-view images without 3D sup…
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
Qi Zuo, Xiaodong Gu, Lingteng Qiu +8
Generating multi-view images based on text or single-image prompts is a critical capability for the creation of 3D content. Two fundamental questions on this topic are what data we…
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes
Zhengyi Zhao, Chen Song, Xiaodong Gu +6
A fundamental problem in the texturing of 3D meshes using pre-trained text-to-image models is to ensure multi-view consistency. State-of-the-art approaches typically use diffusion…
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Lingteng Qiu, Guanying Chen, Xiaodong Gu +7
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images. Existin…