We propose a multimodal 3D scene editing framework MMEditor to create or modify objects within an extant 3D Gaussian Splatting (3DGS) according to text and image prompts. MMEditor employs a multimodal image editing module to iteratively optimize 3D Gaussians in editing regions for delicate and multi-view consistent 3D editing. The key multimodal image editing module can perform editing with accurate appearance and location control, which is achieved by two designs. First, a multimodel adapter block takes the reference image as a foreign language to augment the text prompt, enabling editing results to align with the generic text description and the unique characteristics in the reference image. Second, an attention-based localization block localizes cross-attention with user-defined 3D bounding boxes, thereby ensuring the editing occurs in editing regions. Experiments demonstrate that our method achieves more accurate and controllable results than previous state-of-the-art methods.

Time-lapse videos can visualize the temporal change of dynamic scenes and present wonderful sights with drastic variance in color appearance and rapid movement that interests people. We propose an aesthetics-driven virtual time-lapse photography framework to explore the automatic generation of time-lapse videos in the virtual world, which has potential applications like artistic creation and entertainment in the virtual space. We first define shooting parameters to parameterize the time-lapse photography process and accordingly propose image, video, and time-lapse aesthetic assessments to optimize these parameters, enabling the process to be autonomous and adaptive. We also build an interactive interface to visualize the shooting process and help users conduct virtual time-lapse photography by personalizing shooting parameters according to their aesthetic preferences. Finally, we present a two-stream time-lapse aesthetic model and a time-lapse aesthetic dataset, which can evaluate the aesthetic quality of time-lapse videos. Experimental results demonstrate our method can automatically generate time-lapse videos comparable to those of professional photographers and is more efficient.