← Search

Jiakai Sun

10 accepted papers

2026

MAPo: Motion-Aware Partitioning of Deformable 3D Gaussian Splatting for High-Fidelity Dynamic Scene Reconstruction

CVPR 2026

3D Gaussian Splatting, known for enabling high-quality static scene reconstruction with fast rendering, is increasingly being applied to multi-view dynamic scene reconstruction. A common strategy involves learning a deformation field to model the temporal changes of a canonical set of 3D Gaussians.

Cited by 0SourceScholar
2024

3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos

CVPR 2024highlight

Constructing photo-realistic Free-Viewpoint Videos (FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advancements achieved by current neural rendering techniques these methods generally require complete video sequences for offline training and are…

2024

ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank

AAAI 2024technical

Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. Small model-based approaches can preserve the conte…

2024

Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation

AAAI 2024technical

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image…

Cited by 2SourcePDFScholar
2024

PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping

AAAI 2024technical

3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtai…

Cited by 2SourcePDFScholar
2024

Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model

ECCV 2024poster

"Current video deblurring methods have limitations in recovering high-frequency information since the regression losses are conservative with high-frequency details. Since Diffusion Models (DMs) have strong capabilities in generating high-frequency details, we consider introducing DMs into the video…

2024

Single-Mask Inpainting for Voxel-based Neural Radiance Fields

ECCV 2024poster

"3D inpainting is a challenging task in computer vision and graphics that aims to remove objects and fill in missing regions with a visually coherent and complete representation of the background. A few methods have been proposed to address this problem, yielding notable results in inpainting. Howev…

Cited by 1SourcePDFScholar
2023

Rethinking Fast Fourier Convolution in Image Inpainting

ICCV 2023poster

Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification. FFC empowers the fully convolutional network to have a global receptive field in its early layers. Thanks to the…

Cited by 33PDFScholar
2023

Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale Upsampling

ICCV 2023poster

Recently, several methods have explored the potential of multi-contrast magnetic resonance imaging (MRI) super-resolution (SR) and obtain results superior to single-contrast SR methods. However, existing approaches still have two shortcomings: (1) They can only address fixed integer upsampling scale…

Cited by 22PDFcodeScholar
2023

VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs

IJCAI 2023poster

Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using…