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arXiv · 2610.37446

PolyGandr: Steering Generative Models via Spatial Navigation through Latent Space

Mingwei S. G. Li·Susie S. Y. Li·Isabel Moreira de Oliveira·Ryan P. Adams·Remco Chang
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Abstract

Generative models support content creation across text, images, videos, and design disciplines, yet their interfaces rely primarily on natural language. While effective for specifying high-level intent, language can be inefficient and imprecise for fine-grained control, iterative refinement, and open-ended exploration. We present PolyGandr, a visual interface for exploring relationships between latent representations and generated outputs, complementing natural-language model steering. PolyGandr formulates generative control as high-dimensional navigation, enabling users to define starting points, steering directions, and step sizes through a unified map-and-mixing-palette interface. Combining global exploration with local refinement supports both broad design-space navigation and fine-grained output manipulation without repeatedly translating visual intent into prompts. We evaluate PolyGandr across three use cases: StyleGAN2 face generation, Stable Diffusion chair design, and rotational kirigami engineering. A formative study indicates that PolyGandr complements existing workflows by supporting real-time editing while simultaneously providing an overview and means to explore the broader design space.

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Mingwei S. G. Li, Susie S. Y. Li, Isabel Moreira de Oliveira, Ryan P. Adams, Remco Chang. 2026-10-27. PolyGandr: Steering Generative Models via Spatial Navigation through Latent Space. https://arxiv.org/abs/2610.37446

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