15 citations · 58 across the 22 of their papers we have counts for
5 papers · 2 filters
Semantic Score Distillation Sampling for Compositional Text-to-3D Generation
Ling Yang, Zixiang Zhang, Junlin Han +4
Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art a…
Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation
Junlin Han, Jianyuan Wang, Andrea Vedaldi +2
Generating high-quality 3D content from text, single images, or sparse view images remains a challenging task with broad applications. Existing methods typically employ multi-view…
DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer
Runjia Li, Junlin Han, Luke Melas-Kyriazi +6
We present DreamBeast, a novel method based on score distillation sampling (SDS) for generating fantastical 3D animal assets composed of distinct parts. Existing SDS methods often…
Strong and Controllable Blind Image Decomposition
Zeyu Zhang, Junlin Han, Chenhui Gou +2
Blind image decomposition aims to decompose all components present in an image, typically used to restore a multi-degraded input image. While fully recovering the clean image is ap…
VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models
Junlin Han, Filippos Kokkinos, Philip Torr
This paper presents a novel method for building scalable 3D generative models utilizing pre-trained video diffusion models. The primary obstacle in developing foundation 3D generat…