Image-Space Collage and Packing with Differentiable Rendering

Abstract

Collage and packing techniques are widely used to organize geometric shapes into cohesive visual representations, facilitating the representation of visual features holistically, as seen in image collages and word clouds. Traditional methods often rely on object-space optimization, requiring intricate geometric descriptors and energy functions to handle complex shapes. In this paper, we introduce a versatile image-space collage technique. Leveraging a differentiable renderer, our method effectively optimizes the object layout with image-space losses, bringing the benefit of fixed complexity and easy accommodation of various shapes. Applying a hierarchical resolution strategy in image space, our method efficiently optimizes the collage with fast convergence, large coarse steps first and then small precise steps. The diverse visual expressiveness of our approach is demonstrated through various examples. Experimental results show that our method achieves an order-of-magnitude speedup compared to state-of-the-art techniques. The project page is https://szuviz.github.io/pixel-space-collage-technique/.