  Multimodal synthetic data generation is crucial in domains such as autonomous
driving, robotics, augmented/virtual reality, and retail. We propose a novel
approach, GenMM, for jointly editing RGB videos and LiDAR scans by inserting
temporally and geometrically consistent 3D objects. Our method uses a reference
image and 3D bounding boxes to seamlessly insert and blend new objects into
target videos. We inpaint the 2D Regions of Interest (consistent with 3D boxes)
using a diffusion-based video inpainting model. We then compute semantic
boundaries of the object and estimate it's surface depth using state-of-the-art
semantic segmentation and monocular depth estimation techniques. Subsequently,
we employ a geometry-based optimization algorithm to recover the 3D shape of
the object's surface, ensuring it fits precisely within the 3D bounding box.
Finally, LiDAR rays intersecting with the new object surface are updated to
reflect consistent depths with its geometry. Our experiments demonstrate the
effectiveness of GenMM in inserting various 3D objects across video and LiDAR
modalities.
