Search Results for author: Fangcheng Zhong

Found 10 papers, 2 papers with code

SYM3D: Learning Symmetric Triplanes for Better 3D-Awareness of GANs

no code implementations10 Jun 2024 Jing Yang, Kyle Fogarty, Fangcheng Zhong, Cengiz Oztireli

Despite the growing success of 3D-aware GANs, which can be trained on 2D images to generate high-quality 3D assets, they still rely on multi-view images with camera annotations to synthesize sufficient details from all viewing directions.

Text to 3D

Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping

no code implementations20 May 2024 Tianhao Wu, Jing Yang, Zhilin Guo, Jingyi Wan, Fangcheng Zhong, Cengiz Oztireli

By equipping the most recent 3D Gaussian Splatting representation with head 3D morphable models (3DMM), existing methods manage to create head avatars with high fidelity.

Differentiable Visual Computing for Inverse Problems and Machine Learning

no code implementations21 Nov 2023 Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas, Krishna Murthy Jatavallabhula, Cengiz Oztireli, Tzu-Mao Li, Derek Nowrouzezahrai

This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.

FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation

no code implementations20 Nov 2023 Chenliang Zhou, Fangcheng Zhong, Param Hanji, Zhilin Guo, Kyle Fogarty, Alejandro Sztrajman, Hongyun Gao, Cengiz Oztireli

The improvement in generation quality and diversity is achieved through (1) a novel frequency rectification via spherical harmonics designed to retain high-frequency content while learning the point cloud distribution; and (2) a latent DDPM to learn the regularized yet complex latent distribution.

Computational Efficiency Denoising +2

ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization

no code implementations23 Aug 2023 Wenzhao Li, Tianhao Wu, Fangcheng Zhong, Cengiz Oztireli

We highlight a research gap in radiance fields style transfer, the lack of sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer.

3D Reconstruction Style Transfer

$α$Surf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity

no code implementations17 Mar 2023 Tianhao Wu, Hanxue Liang, Fangcheng Zhong, Gernot Riegler, Shimon Vainer, Cengiz Oztireli

While neural radiance field (NeRF) based methods can model semi-transparency and achieve photo-realistic quality in synthesized novel views, their volumetric geometry representation tightly couples geometry and opacity, and therefore cannot be easily converted into surfaces without introducing artifacts.

Surface Reconstruction

D$^2$NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video

no code implementations31 May 2022 Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole, Cengiz Oztireli

We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background.

Image Segmentation Semantic Segmentation +1

Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration

1 code implementation16 Sep 2020 Param Hanji, Fangcheng Zhong, Rafal K. Mantiuk

A near-optimal reconstruction of the radiance of a High Dynamic Range scene from an exposure stack can be obtained by modeling the camera noise distribution.

Camera Calibration

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