Dejia Xu

We design a novel cascaded generation pipeline that produces 3D Gaussian-based objects without per-instance optimization. Our Amortized Generative 3D Gaussians (AGG) framework involves a coarse generator that predicts a hybrid representation for 3D Gaussians at a low resolution and a super-resolution module that delivers dense 3D Gaussians in the fine stage.

We introduce grounded 4D content generation. We identify static 3D assets and monocular video sequences as key components in constructing the 4D content. Our pipeline facilitates conditional 4D generation, enabling users to specify geometry (3D assets) and motion (monocular videos), thus offering superior control over content creation.

We derive an entropy-maximizing score distillation rule that fosters view diversity and address the multi-face problem for text-to-3D generation.

We transform 3D Gaussians into a more efficient and compact format.