VideoRepair: Improving Text-to-Video Generation via Misalignment
  Evaluation and Localized Refinement
Authors: name: Daeun Lee, name: Jaehong Yoon, name: Jaemin Cho, name: Mohit Bansal

Recent text-to-video (T2V) diffusion models have demonstrated impressive
generation capabilities across various domains. However, these models often
generate videos that have misalignments with text prompts, especially when the
prompts describe complex scenes with multiple objects and attributes. To
address this, we introduce VideoRepair, a novel model-agnostic, training-free
video refinement framework that automatically identifies fine-grained
text-video misalignments and generates explicit spatial and textual feedback,
enabling a T2V diffusion model to perform targeted, localized refinements.
VideoRepair consists of four stages: In (1) video evaluation, we detect
misalignments by generating fine-grained evaluation questions and answering
those questions with MLLM. In (2) refinement planning, we identify accurately
generated objects and then create localized prompts to refine other areas in
the video. Next, in (3) region decomposition, we segment the correctly
generated area using a combined grounding module. We regenerate the video by
adjusting the misaligned regions while preserving the correct regions in (4)
localized refinement. On two popular video generation benchmarks (EvalCrafter
and T2V-CompBench), VideoRepair substantially outperforms recent baselines
across various text-video alignment metrics. We provide a comprehensive
analysis of VideoRepair components and qualitative examples.