most citedMaterial Anything: Generating Materials for Any 3D Object via Diffusion

2 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2025

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…

cs.CV2024

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…

cs.CV2024★ 2 cited

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…

cs.CV2024

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…

cs.CV2024

Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation

Mengchen Zhang, Tong Wu, Tai Wang +3

6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation fr…

cs.CV2024★ 1 cited

ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance

Yongwei Chen, Tengfei Wang, Tong Wu +3

Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward m…

cs.CV2024★ 2 cited

ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars

Zhenwei Wang, Tengfei Wang, Gerhard Hancke +2

Real-world applications often require a large gallery of 3D assets that share a consistent theme. While remarkable advances have been made in general 3D content creation from text…