  Graphics design is important for various applications, including movie
production and game design. To create a high-quality scene, designers usually
need to spend hours in software like Blender, in which they might need to
interleave and repeat operations, such as connecting material nodes, hundreds
of times. Moreover, slightly different design goals may require completely
different sequences, making automation difficult. In this paper, we propose a
system that leverages Vision-Language Models (VLMs), like GPT-4V, to
intelligently search the design action space to arrive at an answer that can
satisfy a user's intent. Specifically, we design a vision-based edit generator
and state evaluator to work together to find the correct sequence of actions to
achieve the goal. Inspired by the role of visual imagination in the human
design process, we supplement the visual reasoning capabilities of VLMs with
"imagined" reference images from image-generation models, providing visual
grounding of abstract language descriptions. In this paper, we provide
empirical evidence suggesting our system can produce simple but tedious Blender
editing sequences for tasks such as editing procedural materials and geometry
from text and/or reference images, as well as adjusting lighting configurations
for product renderings in complex scenes.
