CIVITAI / Workflows

VACE SkyReels V3 R2V Merge Skeleton-Guided Video Workflow

This workflow is designed for VACE + SkyReels V3 R2V Merge skeleton-guided video generation. Its main purpose is to take a reference character or source image, combine it with a motion / pose-guidance video, and generate a new video where the subject follows the skeleton-driven movement while preserving a stronger visual identity and cinematic style. It is especially useful for creators who want more controlled character motion instead of relying only on text prompts or random video generation. The workflow uses a video-loading and motion-reference structure. VHS_LoadVideo imports the guide video, extracts frames, reads video information such as width, height, frame count, and audio, then passes this information into the generation and output stages. This is important because skeleton-guided video workflows need the generated result to follow the timing and motion structure of the input video. The workflow also keeps audio routing available, so the final exported video can preserve the source audio when needed. On the visual side, the workflow uses reference images and image resizing nodes to prepare the character or visual identity before generation. ImageResizeKJv2 and ImageResize+ help align the reference image and guide-video dimensions, making the input more compatible with the video generation route. The workflow also uses image batching and comparison layouts, allowing users to check reference images, generated frames, and side-by-side outputs more clearly. The generation route is built around a VACE / SkyReels-style video pipeline with Wan-family components. It uses UMT5-style text encoding, Wan VAE decoding, positive and negative prompt conditioning, KSampler generation, VAEDecode, and VHS_VideoCombine for final MP4 output. The positive prompt controls the subject, scene, style, and action direction, while the negative prompt helps suppress common video artifacts such as broken limbs, unstable anatomy, flicker, low quality, or inconsistent motion. The key value of this workflow is skeleton-driven motion transfer. Instead of asking the model to invent an action from text alone, the workflow uses the guide video as a motion structure. This makes it more suitable for dance videos, martial arts motion, character performance, action clips, stylized animation, AI influencer videos, cosplay transformation, game-character motion tests, and cinematic sho