28 citations · 40 across the 11 of their papers we have counts for
11 papers
SEGAR: Selective Enhancement for Generative Augmented Reality
Fanjun Bu, Chenyang Yuan, Hiroshi Yasuda
Generative world models offer a compelling foundation for augmented-reality (AR) applications: by predicting future image sequences that incorporate deliberate visual edits, they e…
Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
Chaoyi Pan, Giri Anantharaman, Nai-Chieh Huang +8
Generative models, like flows and diffusions, have recently emerged as popular and efficacious policy parameterizations in robotics. There has been much speculation as to the facto…
Stylish and Functional: Guided Interpolation Subject to Physical Constraints
Yan-Ying Chen, Nikos Arechiga, Chenyang Yuan +3
Generative AI is revolutionizing engineering design practices by enabling rapid prototyping and manipulation of designs. One example of design manipulation involves taking two refe…
Parametric-ControlNet: Multimodal Control in Foundation Models for Precise Engineering Design Synthesis
Rui Zhou, Yanxia Zhang, Chenyang Yuan +4
This paper introduces a generative model designed for multimodal control over text-to-image foundation generative AI models such as Stable Diffusion, specifically tailored for engi…
Enhancing Sample Generation of Diffusion Models using Noise Level Correction
Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu +1
The denoising process of diffusion models can be interpreted as an approximate projection of noisy samples onto the data manifold. Moreover, the noise level in these samples approx…
VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling
Hayata Morita, Kohei Shintani, Chenyang Yuan +1
A main challenge in mechanical design is to efficiently explore the design space while satisfying engineering constraints. This work explores the use of 3D generative models to exp…
Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models
Rui Zhou, Chenyang Yuan, Frank Permenter +4
This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs.…
Drag-guided diffusion models for vehicle image generation
Nikos Arechiga, Frank Permenter, Binyang Song +1
Denoising diffusion models trained at web-scale have revolutionized image generation. The application of these tools to engineering design is an intriguing possibility, but is curr…
Interpreting and Improving Diffusion Models from an Optimization Perspective
Frank Permenter, Chenyang Yuan
Denoising is intuitively related to projection. Indeed, under the manifold hypothesis, adding random noise is approximately equivalent to orthogonal perturbation. Hence, learning t…
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings
Binyang Song, Chenyang Yuan, Frank Permenter +2
Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization…
FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
Liang Xu, Xiaojing Lu, Chenyang Yuan +8
Pretrained Language Models (PLMs) have achieved tremendous success in natural language understanding tasks. While different learning schemes -- fine-tuning, zero-shot, and few-shot…