2 citations · 2 across the 3 of their papers we have counts for
5 papers
3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models
Yuhan Zhang, Mengchen Zhang, Tong Wu +4
3D generation is experiencing rapid advancements, while the development of 3D evaluation has not kept pace. How to keep automatic evaluation equitably aligned with human perception…
Neural LightRig: Unlocking Accurate Object Normal and Material Estimation with Multi-Light Diffusion
Zexin He, Tengfei Wang, Xin Huang +2
Recovering the geometry and materials of objects from a single image is challenging due to its under-constrained nature. In this paper, we present Neural LightRig, a novel framewor…
Material Anything: Generating Materials for Any 3D Object via Diffusion
Xin Huang, Tengfei Wang, Ziwei Liu +1
We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on co…
SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE
Yongwei Chen, Yushi Lan, Shangchen Zhou +2
Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, mov…
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion
Zhenwei Wang, Tengfei Wang, Zexin He +3
In 3D modeling, designers often use an existing 3D model as a reference to create new ones. This practice has inspired the development of Phidias, a novel generative model that use…