title: Motion-Aware Concept Alignment for Consistent Video Editing
summary: 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.
paper_authors._id: 68403832646314cfc20a5de2
paper_authors.name: Tong Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T15:02:54.641Z
paper_authors._id: 65c898197faf326d067e2c0d
paper_authors.avatarUrl: /avatars/dde3c047e627bacd69e0f063a4a833b2.svg
paper_authors.fullname: Tong Zhang
paper_authors.type: user
paper_authors.user: Tong98Zhang
paper_authors._id: 68403832646314cfc20a5de3
paper_authors.name: Juan C Leon Alcazar
paper_authors._id: 68403832646314cfc20a5de4
paper_authors.name: Bernard Ghanem
paper_title: Motion-Aware Concept Alignment for Consistent Video Editing
paper_summary: 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.
paper_ai_summary: MoCA-Video injects semantic features from a reference image into a video object, preserving motion and visual context, and outperforms baselines using novel metrics.
paper_ai_keywords: MoCA-Video Motion-Aware Concept Alignment image-domain semantic mixing diagonal denoising schedule class-agnostic segmentation latent space momentum-based semantic corrections gamma residual noise stabilization SSIM image-level LPIPS temporal LPIPS CASS Conceptual Alignment Shift Score diffusion noise trajectory video synthesis structured manipulation