TESLA: Native 4D Gaussian Splatting Generation with Temporally Structured Latents

Diffusion models have achieved remarkable success in image, video, and 3D content creation. However, extending these successes to native 4D model generation remains challenging due to the intricate coupling between temporal geometry and appearance dynamics. Existing approaches typically generate 4D content by synthesizing videos from novel viewpoints, yet they often suffer from poor spatio-temporal consistency across views and time. To address these limitations, we introduce TESLA, a novel feedforward approach for generating native 4D Gaussian Splatting (4DGS) representations. Our key insight is to decouple temporal geometry and appearance generation through a temporally structured latent representation. This decomposition allows us to first establish coarse temporal geometry that captures fundamental structural dynamics, and then synthesize fine-grained appearance details conditioned on this geometric scaffold. To bridge this decoupled representation to the final output, we design a native 4DGS decoder that directly transforms our temporal features into native 4DGS representations, enabling flexible temporal rendering across arbitrary viewpoints and timesteps. Trained on a large collection of curated animated sequences, we extensively evaluate TESLA on the 4DGS generation task, showing superior performance over existing baselines.