2 citations · 5 across the 23 of their papers we have counts for
9 papers · 1 filter
3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
Yihang Luo, Shangchen Zhou, Yushi Lan +2
Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model gener…
Trajectory Attention for Fine-grained Video Motion Control
Zeqi Xiao, Wenqi Ouyang, Yifan Zhou +4
Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized v…
Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation
Tianyi Wei, Dongdong Chen, Yifan Zhou +1
Representing the cutting-edge technique of text-to-image models, the latest Multimodal Diffusion Transformer (MMDiT) largely mitigates many generation issues existing in previous m…
GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation
Yushi Lan, Shangchen Zhou, Zhaoyang Lyu +5
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper intro…
MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors
Honghua Chen, Yushi Lan, Yongwei Chen +2
Drag-based editing has become popular in 2D content creation, driven by the capabilities of image generative models. However, extending this technique to 3D remains a challenge. Ex…
I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models
Wenqi Ouyang, Yi Dong, Lei Yang +2
The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional chall…
MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior
Honghua Chen, Chen Change Loy, Xingang Pan
Despite the emergence of successful NeRF inpainting methods built upon explicit RGB and depth 2D inpainting supervisions, these methods are inherently constrained by the capabiliti…
Video Diffusion Models are Training-free Motion Interpreter and Controller
Zeqi Xiao, Yifan Zhou, Shuai Yang +1
Video generation primarily aims to model authentic and customized motion across frames, making understanding and controlling the motion a crucial topic. Most diffusion-based studie…
LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
Yushi Lan, Fangzhou Hong, Shangchen Zhou +7
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved su…