KnobGen introduces a novel dual-pathway framework for sketch-based image generation, designed to adapt to varying levels of user skill and sketch complexity. By leveraging a Coarse-Grained Controller for high-level semantic understanding and a Fine-Grained Controller for detailed refinement, KnobGen ensures precise yet flexible image generation across a broad spectrum of sketch inputs. A key innovation in the framework is the inference-time knob, which allows users to dynamically control the level of adherence to the input sketch. This approach democratizes image generation by effectively balancing fine-grained precision and broader spatial structure, making it suitable for both amateur and professional use.

Segformer3D is a light-weight and efficient hierarchical Transformer designed for 3D volumetric segmentation. It calculates attention across multiscale volumetric features, and avoids complex decoders. Instead it uses a simple yet effective all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks.