  Recent breakthroughs in text-to-4D generation rely on pre-trained
text-to-image and text-to-video models to generate dynamic 3D scenes. However,
current text-to-4D methods face a three-way tradeoff between the quality of
scene appearance, 3D structure, and motion. For example, text-to-image models
and their 3D-aware variants are trained on internet-scale image datasets and
can be used to produce scenes with realistic appearance and 3D structure -- but
no motion. Text-to-video models are trained on relatively smaller video
datasets and can produce scenes with motion, but poorer appearance and 3D
structure. While these models have complementary strengths, they also have
opposing weaknesses, making it difficult to combine them in a way that
alleviates this three-way tradeoff. Here, we introduce hybrid score
distillation sampling, an alternating optimization procedure that blends
supervision signals from multiple pre-trained diffusion models and incorporates
benefits of each for high-fidelity text-to-4D generation. Using hybrid SDS, we
demonstrate synthesis of 4D scenes with compelling appearance, 3D structure,
and motion.
