most citedRenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars

5 citations · 7 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior

Fan Lu, Kwan-Yee Lin, Yan Xu +3

Text-to-3D generation has achieved remarkable success via large-scale text-to-image diffusion models. Nevertheless, there is no paradigm for scaling up the methodology to urban sca…

cs.CV2024

CosmicMan: A Text-to-Image Foundation Model for Humans

Shikai Li, Jianglin Fu, Kaiyuan Liu +3

We present CosmicMan, a text-to-image foundation model specialized for generating high-fidelity human images. Unlike current general-purpose foundation models that are stuck in the…

cs.CV20231 cited

UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation

Jianglin Fu, Shikai Li, Yuming Jiang +3

Human generation has achieved significant progress. Nonetheless, existing methods still struggle to synthesize specific regions such as faces and hands. We argue that the main reas…

cs.CV2023

Urban Radiance Field Representation with Deformable Neural Mesh Primitives

Fan Lu, Yan Xu, Guang Chen +3

Neural Radiance Fields (NeRFs) have achieved great success in the past few years. However, most current methods still require intensive resources due to ray marching-based renderin…

cs.CV20235 cited

RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars

Dongwei Pan, Long Zhuo, Jingtan Piao +13

Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still fac…

cs.CV20231 cited

MonoHuman: Animatable Human Neural Field from Monocular Video

Zhengming Yu, Wei Cheng, Xian Liu +2

Animating virtual avatars with free-view control is crucial for various applications like virtual reality and digital entertainment. Previous studies have attempted to utilize the…