Motion-Aware Concept Alignment for Consistent Video Editing
Authors: name: Tong Zhang, name: Juan C Leon Alcazar, name: Bernard Ghanem

We introduce MoCA-Video (Motion-Aware Concept Alignment in Video), a
training-free framework bridging the gap between image-domain semantic mixing
and video. Given a generated video and a user-provided reference image,
MoCA-Video injects the semantic features of the reference image into a specific
object within the video, while preserving the original motion and visual
context. Our approach leverages a diagonal denoising schedule and
class-agnostic segmentation to detect and track objects in the latent space and
precisely control the spatial location of the blended objects. To ensure
temporal coherence, we incorporate momentum-based semantic corrections and
gamma residual noise stabilization for smooth frame transitions. We evaluate
MoCA's performance using the standard SSIM, image-level LPIPS, temporal LPIPS,
and introduce a novel metric CASS (Conceptual Alignment Shift Score) to
evaluate the consistency and effectiveness of the visual shifts between the
source prompt and the modified video frames. Using self-constructed dataset,
MoCA-Video outperforms current baselines, achieving superior spatial
consistency, coherent motion, and a significantly higher CASS score, despite
having no training or fine-tuning. MoCA-Video demonstrates that structured
manipulation in the diffusion noise trajectory allows for controllable,
high-quality video synthesis.