DreamRunner: Fine-Grained Storytelling Video Generation with
  Retrieval-Augmented Motion Adaptation
Authors: name: Zun Wang, name: Jialu Li, name: Han Lin, name: Jaehong Yoon, name: Mohit Bansal

Storytelling video generation (SVG) has recently emerged as a task to create
long, multi-motion, multi-scene videos that consistently represent the story
described in the input text script. SVG holds great potential for diverse
content creation in media and entertainment; however, it also presents
significant challenges: (1) objects must exhibit a range of fine-grained,
complex motions, (2) multiple objects need to appear consistently across
scenes, and (3) subjects may require multiple motions with seamless transitions
within a single scene. To address these challenges, we propose DreamRunner, a
novel story-to-video generation method: First, we structure the input script
using a large language model (LLM) to facilitate both coarse-grained scene
planning as well as fine-grained object-level layout and motion planning. Next,
DreamRunner presents retrieval-augmented test-time adaptation to capture target
motion priors for objects in each scene, supporting diverse motion
customization based on retrieved videos, thus facilitating the generation of
new videos with complex, scripted motions. Lastly, we propose a novel
spatial-temporal region-based 3D attention and prior injection module SR3AI for
fine-grained object-motion binding and frame-by-frame semantic control. We
compare DreamRunner with various SVG baselines, demonstrating state-of-the-art
performance in character consistency, text alignment, and smooth transitions.
Additionally, DreamRunner exhibits strong fine-grained condition-following
ability in compositional text-to-video generation, significantly outperforming
baselines on T2V-ComBench. Finally, we validate DreamRunner's robust ability to
generate multi-object interactions with qualitative examples.