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Tengfei Wang

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
ORCID 0000-0003-4079-0687
same name
  • Tengfei Wang — 4 papers
  • Tengfei Wang — 2 papers
  • Tengfei Wang — 1 paper
  • Tengfei Wang — 1 paper
  • Tengfei Wang — 1 paper, h 1
  • Tengfei Wang — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

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

2 citations · 4 across the 4 of their papers we have counts for

collaborators

4 papers

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★ 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★ 1 cited

3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors

Fangzhou Hong, Jiaxiang Tang, Ziang Cao +8

We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage…

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