12 citations · 12 across the 11 of their papers we have counts for
11 papers
Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion
Zhenggang Tang, Yuehao Wang, Yuchen Fan +9
Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate…
Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image -- Technical Preview
Jun-Kun Chen, Aayush Bansal, Minh Phuoc Vo +1
We introduce the Virtual Fitting Room (VFR), a novel video generative model that produces arbitrarily long virtual try-on videos. Our VFR models long video generation tasks as an a…
Dress&Dance: Dress up and Dance as You Like It - Technical Preview
Jun-Kun Chen, Aayush Bansal, Minh Phuoc Vo +1
We present Dress&Dance, a video diffusion framework that generates high quality 5-second-long 24 FPS virtual try-on videos at 1152x720 resolution of a user wearing desired garments…
V2Edit: Versatile Video Diffusion Editor for Videos and 3D Scenes
Yanming Zhang, Jun-Kun Chen, Jipeng Lyu +1
This paper introduces VEdit, a novel training-free framework for instruction-guided video and 3D scene editing. Addressing the critical challenge of balancing original content…
ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing
Jun-Kun Chen, Yu-Xiong Wang
This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the cru…
SceneCraft: Layout-Guided 3D Scene Generation
Xiuyu Yang, Yunze Man, Jun-Kun Chen +1
The creation of complex 3D scenes tailored to user specifications has been a tedious and challenging task with traditional 3D modeling tools. Although some pioneering methods have…
ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity Scene Editing
Jun-Kun Chen, Samuel Rota Bulò, Norman Müller +3
This paper proposes ConsistDreamer - a novel framework that lifts 2D diffusion models with 3D awareness and 3D consistency, thus enabling high-fidelity instruction-guided scene edi…
Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion
Linzhan Mou, Jun-Kun Chen, Yu-Xiong Wang
This paper proposes Instruct 4D-to-4D that achieves 4D awareness and spatial-temporal consistency for 2D diffusion models to generate high-quality instruction-guided dynamic scene…
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds
Jun-Kun Chen, Jipeng Lyu, Yu-Xiong Wang
This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synth…
PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees
Jun-Kun Chen, Yu-Xiong Wang
Being able to learn an effective semantic representation directly on raw point clouds has become a central topic in 3D understanding. Despite rapid progress, state-of-the-art encod…
Is Self-Supervised Learning More Robust Than Supervised Learning?
Yuanyi Zhong, Haoran Tang, Junkun Chen +2
Self-supervised contrastive learning is a powerful tool to learn visual representation without labels. Prior work has primarily focused on evaluating the recognition accuracy of va…