DreamColour: Controllable Video Colour Editing without Training

Video colour editing is a crucial task for content creation, yet existing solutions either require painstaking frame-by-frame manipulation or produce unrealistic results with temporal artefacts. We present a practical, training-free framework that makes precise video colour editing accessible through an intuitive interface while maintaining professional-quality output. Our key insight is that by decoupling spatial and temporal aspects of colour editing, we can better align with users' natural workflow -- allowing them to focus on precise colour selection in key frames before automatically propagating changes across time. We achieve this through a novel technical framework that combines: (i) a simple point-and-click interface merging grid-based colour selection with automatic instance segmentation for precise spatial control, (ii) bidirectional colour propagation that leverages inherent video motion patterns, and (iii) motion-aware blending that ensures smooth transitions even with complex object movements. Through extensive evaluation on diverse scenarios, we demonstrate that our approach matches or exceeds state-of-the-art methods while eliminating the need for training or specialized hardware, making professional-quality video colour editing accessible to everyone.

SketchDeco: Decorating B&W Sketches with Colour

This paper introduces a novel approach to sketch colourisation, inspired by the universal childhood activity of colouring and its professional applications in design and story-boarding. Striking a balance between precision and convenience, our method utilises region masks and colour palettes to allow intuitive user control, steering clear of the meticulousness of manual colour assignments or the limitations of textual prompts. By strategically combining ControlNet and staged generation, incorporating Stable Diffusion v1.5, and leveraging BLIP-2 text prompts, our methodology facilitates faithful image generation and user-directed colourisation. Addressing challenges of local and global consistency, we employ inventive solutions such as an inversion scheme, guided sampling, and a self-attention mechanism with a scaling factor. The resulting tool is not only fast and training-free but also compatible with consumer-grade Nvidia RTX 4090 Super GPUs, making it a valuable asset for both creative professionals and enthusiasts in various fields. Project Page: \url{https://chaitron.github.io/SketchDeco/}