  Directly learning to model 4D content, including shape, color and motion, is
challenging. Existing methods depend on skeleton-based motion control and offer
limited continuity in detail. To address this, we propose a novel framework
that generates coherent 4D sequences with animation of 3D shapes under given
conditions with dynamic evolution of shape and color over time through
integrative latent mapping. We first employ an integrative latent unified
representation to encode shape and color information of each detailed 3D
geometry frame. The proposed skeleton-free latent 4D sequence joint
representation allows us to leverage diffusion models in a low-dimensional
space to control the generation of 4D sequences. Finally, temporally coherent
4D sequences are generated conforming well to the input images and text
prompts. Extensive experiments on the ShapeNet, 3DBiCar and DeformingThings4D
datasets for several tasks demonstrate that our method effectively learns to
generate quality 3D shapes with color and 4D mesh animations, improving over
the current state-of-the-art. Source code will be released.
