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20222024
most citedStyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation

13 citations · 25 across the 8 of their papers we have counts for

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cs.CV20246 cited

MeshXL: Neural Coordinate Field for Generative 3D Foundation Models

Sijin Chen, Xin Chen, Anqi Pang +11

The polygon mesh representation of 3D data exhibits great flexibility, fast rendering speed, and storage efficiency, which is widely preferred in various applications. However, giv…

cs.CV2024

EMMA: Your Text-to-Image Diffusion Model Can Secretly Accept Multi-Modal Prompts

Yucheng Han, Rui Wang, Chi Zhang +4

Recent advancements in image generation have enabled the creation of high-quality images from text conditions. However, when facing multi-modal conditions, such as text combined wi…

cs.CV20244 cited

ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Xiwei Hu, Rui Wang, Yixiao Fang +3

Diffusion models have demonstrated remarkable performance in the domain of text-to-image generation. However, most widely used models still employ CLIP as their text encoder, which…

cs.CV2023

Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering

Chi Zhang, Wei Yin, Gang Yu +5

In this study, we address the challenge of 3D scene structure recovery from monocular depth estimation. While traditional depth estimation methods leverage labeled datasets to dire…

cs.CV2023

Deformation Robust Text Spotting with Geometric Prior

Xixuan Hao, Aozhong Zhang, Xianze Meng +1

The goal of text spotting is to perform text detection and recognition in an end-to-end manner. Although the diversity of luminosity and orientation in scene texts has been widely…

cs.CV202313 cited

StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation

Chi Zhang, Yiwen Chen, Yijun Fu +7

The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D r…