Presentation
An Efficient Framework for High-Fidelity Automotive Exterior Design
DescriptionExterior design plays an important role in automotive design industries and it usually takes laborious work by designers. Image editing, as a fundamental image manipulation task, has been revolutionized by denoising diffusion models thanks to its great productivity and creativity. However, the application of denoising diffusion models for image editing on automotive design is still limited due to the ambiguous editing instructions and uncontrollable output, leading to undesirable results with bad quality. Moreover, the training and inference require a lot of resources. In this work, we propose a novel image editing framework for automotive design to precisely comprehend human instructions and produce high-fidelity exterior renderings. Meanwhile, it needs only 6.5 GPU hours and 16GB VRAM to train and 8GB VRAM to inference, making it more accessible.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security