most citedInstant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model

32 citations · 61 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20237 cited

PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction

Peng Wang, Hao Tan, Sai Bi +6

We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating…

cs.CV202332 cited

Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model

Jiahao Li, Hao Tan, Kai Zhang +7

Text-to-3D with diffusion models has achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from…

cs.CV202318 cited

DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model

Yinghao Xu, Hao Tan, Fujun Luan +8

We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model inco…

cs.CV2023

Controllable Dynamic Appearance for Neural 3D Portraits

ShahRukh Athar, Zhixin Shu, Zexiang Xu +4

Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and vie…

cs.CV20231 cited

I-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs

Jingsen Zhu, Yuchi Huo, Qi Ye +8

In this work, we present I-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields…

cs.CV20233 cited

PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields

Zhengfei Kuang, Fujun Luan, Sai Bi +3

Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the app…