Ref-GS: Directional Factorization for 2D Gaussian Splatting

Youjia Zhang, Anpei Chen, Yumin Wan, Zikai Song, Junqing Yu, Yawei Luo, Wei Yang

Project

• We propose Ref-GS, a method for directional light factorization in 2D Gaussian splatting, enabling photorealistic rendering and accurate geometry recovery. By applying directional encoding and spherical mip-grids, it reduces ambiguity and enhances roughness-aware shading. Our approach minimizes overhead and improves performance across open-world scenes.

Dynamic feature pruning and consolidation for occluded person re-identification

YuTeng Ye, Jiale Cai, Chenxing Gao, Youjia Zhang, Junle Wang, Qiang Hu, Junqing Yu, Wei Yang

Code

• We propose a Feature Pruning and Consolidation (FPC) framework to circumvent explicit human structure parse, which consists of a sparse encoder, a global and local feature ranking module, and a feature consolidation decoder.

Progressive Text-to-Image Diffusion with Soft Latent Direction

YuTeng Ye, Jiale Cai, Hang Zhou, Guanwen Li, Youjia Zhang, Zikai Song, Chenxing Gao, Junqing Yu, Wei Yang

Code

• We propose to harness the capabilities of a Large Language Model (LLM) to decompose text descriptions into coherent directives adhering to stringent formats and progressively generate the target image.

AMD: anatomical motion diffusion with interpretable motion decomposition and fusion

Beibei Jing, Youjia Zhang, Zikai Song, Junqing Yu, Wei Yang

• We propose the Adaptable Motion Diffusion (AMD) model, which leverages a Large Language Model (LLM) to parse the input text into a sequence of concise and interpretable anatomical scripts that correspond to the target motion.

NeMF: Inverse volume rendering with neural microflake field

Youjia Zhang, Teng Xu, Junqing Yu, Yuteng Ye, Yanqing Jing, Junle Wang, Jingyi Yu, Wei Yang

Project

• We propose to conduct inverse volume rendering by representing a scene using microflake volume, which assumes the space is filled with infinite small flakes and light reflects or scattersat each spatial location according to microflake distributions.

Highly accurate and large-scale collision cross sections prediction with graph neural networks

Renfeng Guo*, Youjia Zhang*, Yuxuan Liao*, Qiong Yang, Ting Xie, Xiaqiong Fan, Zhonglong Lin, Yi Chen, Hongmei Lu, Zhimin Zhang