Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, simplifying the process of producing diverse and individual content. Current methods excel in generating short videos (up to 16s), but produce hard-cuts when naively extended to long video synthesis. To overcome these limitations, we present StreamingT2V, an autoregressive method that generates long videos of up to 2 minutes or longer with seamless transitions. The key components are: (i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the preceding chunk via an attentional mechanism, leading to consistent chunk transitions, (ii) a longterm memory block called appearance preservation module (APM), which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene, and (iii) a randomized blending approach that allows for the autoregressive application of a video enhancer on videos of indefinite length, ensuring consistency across chunks. Experiments show that StreamingT2V produces more motion, while competing methods suffer from video stagnation when applied naively in an autoregressive fashion. Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator, surpassing competitors in both consistency and motion.

Text-guided Image Editing has recently attracted significant attention due to advances in the denoising diffusion models field. Current methods make it possible to execute complex image editing operations with simple text prompts. But despite impressive results, they often fail to restrict the edit area to only the object of interest, specified in the text prompt. To this end, we propose a novel framework we name Grounded-Instruct-Pix2Pix, which is capable of localized instruction-guided image editing in various scenarios including multi-object cases and complex backgrounds. Our experiments on a diverse set of images clearly showcase its advantage over the recent state-of-the-art approaches, especially at restricting the editing effect to the area of interest only. Grounded-Instruct-Pix2Pix implementation will be available at https://github.com/arthur-71/Grounded-Instruct-Pix2Pix.

Low-light enhancement is an increasingly important function in image editing and visual creation. Most existing enhancing algorithms are trained to enlighten a given image in a globally homogeneous way, and (implicitly) to some predefined extent of brightness. They are neither capable of enhancing only local regions of interest ("where") while keeping the overall visual appearance plausible, nor producing outputs at a range of different illumination levels ("how much"). Those hurdles significantly limit the prospect of flexible, customizable, or even user-interactive low-light enhancement. To address these gaps, we propose Region-Controllable Robust Light Enhancement (ReCoRo), a novel framework that allows users to directly specify "where" and "how much" they want to enhance from an input low-light image; meanwhile, the model will learn to intelligently maintain the overall consistent visual appearance and plausible composition via a discriminator. Moreover, since in practical mobile APPs, such user specifications often come in imprecise forms (e.g., finger-drawn masks), we propose to bake in domain-specific data augmentations into training ReCoRo, so that the learned model can gain resilience to various roughly-supplied user masks. Up to our best knowledge, ReCoRo is the first of its kind that allows the user to localize the enlightenment region as well as to control the light intensity. Extensive experiments clearly demonstrate that ReCoRo outperforms state-of-the-art methods in terms of qualitative results, quantitative metrics, and versatile controllability. Project repository: https://bit.ly/ReCoRo-lowlight.