Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptions
This addresses the challenge for 3D artists of accelerating content creation by enabling zero-shot generation from open-world concepts, though it appears incremental as it builds on existing foundation models.
The paper tackles the problem of generating stylized 3D assets from abstract scene descriptions without explicit object enumeration, using a system that combines foundation models like LLMs and diffusion models, and shows through human evaluations that it produces outputs more faithful to the input semantics in 91% of cases compared to a baseline.
What constitutes the "vibe" of a particular scene? What should one find in "a busy, dirty city street", "an idyllic countryside", or "a crime scene in an abandoned living room"? The translation from abstract scene descriptions to stylized scene elements cannot be done with any generality by extant systems trained on rigid and limited indoor datasets. In this paper, we propose to leverage the knowledge captured by foundation models to accomplish this translation. We present a system that can serve as a tool to generate stylized assets for 3D scenes described by a short phrase, without the need to enumerate the objects to be found within the scene or give instructions on their appearance. Additionally, it is robust to open-world concepts in a way that traditional methods trained on limited data are not, affording more creative freedom to the 3D artist. Our system demonstrates this using a foundation model "team" composed of a large language model, a vision-language model and several image diffusion models, which communicate using an interpretable and user-editable intermediate representation, thus allowing for more versatile and controllable stylized asset generation for 3D artists. We introduce novel metrics for this task, and show through human evaluations that in 91% of the cases, our system outputs are judged more faithful to the semantics of the input scene description than the baseline, thus highlighting the potential of this approach to radically accelerate the 3D content creation process for 3D artists.