no code implementations • 20 Dec 2023 • Beibei Jing, Youjia Zhang, Zikai Song, Junqing Yu, Wei Yang
Generating realistic human motion sequences from text descriptions is a challenging task that requires capturing the rich expressiveness of both natural language and human motion. Recent advances in diffusion models have enabled significant progress in human motion synthesis. However, existing methods struggle to handle text inputs that describe complex or long motions. In this paper, 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. This process exploits the LLM's ability to provide anatomical guidance for complex motion synthesis. We then devise a two-branch fusion scheme that balances the influence of the input text and the anatomical scripts on the inverse diffusion process, which adaptively ensures the semantic fidelity and diversity of the synthesized motion. Our method can effectively handle texts with complex or long motion descriptions, where existing methods often fail.
no code implementations • 11 Dec 2023 • Youjia Zhang, Zikai Song, Junqing Yu, Yawei Luo, Wei Yang
We leverage the rendered views from the optimized radiance field as the basis and develop a two-step specialization process of a 2D diffusion model, which is adept at conducting object-specific denoising and generating high-quality multi-view images.
1 code implementation • 27 Nov 2023 • Yuteng Ye, Guanwen Li, Hang Zhou, Cai Jiale, Junqing Yu, Yawei Luo, Zikai Song, Qilong Xing, Youjia Zhang, Wei Yang
A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes.
1 code implementation • 18 Sep 2023 • Yuteng Ye, Jiale Cai, Hang Zhou, Guanwen Li, Youjia Zhang, Zikai Song, Chenxing Gao, Junqing Yu, Wei Yang
In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges.
no code implementations • 2 Jul 2023 • Jianxun Ren, Ning An, Youjia Zhang, Danyang Wang, Zhenyu Sun, Cong Lin, Weigang Cui, Weiwei Wang, Ying Zhou, Wei zhang, Qingyu Hu, Ping Zhang, Dan Hu, Danhong Wang, Hesheng Liu
Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals.
no code implementations • ICCV 2023 • Youjia Zhang, Teng Xu, Junqing Yu, Yuteng Ye, Junle Wang, Yanqing Jing, Jingyi Yu, Wei Yang
Recovering the physical attributes of an object's appearance from its images captured under an unknown illumination is challenging yet essential for photo-realistic rendering.
1 code implementation • 27 Nov 2022 • Yuteng Ye, Hang Zhou, Jiale Cai, Chenxing Gao, Youjia Zhang, Junle Wang, Qiang Hu, Junqing Yu, Wei Yang
The framework mainly consists of a sparse encoder, a multi-view feature mathcing module, and a feature consolidation decoder.
1 code implementation • 19 Oct 2022 • Youjia Zhang, Soyun Choi, Sungeun Hong
Crowd counting research has made significant advancements in real-world applications, but it remains a formidable challenge in cross-modal settings.