Updated: May 10, 2026
character
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 short-form content. The user can keep a character concept or visual reference while borrowing movement from another video.
The workflow also includes comparison output logic. This is useful for presentation because users can view the original guide, the generated result, and reference materials together. For Civitai or RunningHub publishing, this makes the workflow easier to understand: users can immediately see how the skeleton or motion reference affects the final video.
In short, this workflow is not just a normal image-to-video tool. It is a controlled motion-guided video pipeline that combines reference identity, guide-video motion, VACE / SkyReels generation, prompt conditioning, and final video export. If you want to see how the motion guide is loaded, how the reference image is prepared, and how the final skeleton-guided video is generated, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2025493219651362817?inviteCode=rh-v1111
Open the link above to run the workflow directly online and view the generation results in real time.
If the results meet your expectations, you can also deploy it locally for further customization.
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📺 Bilibili Updates (Mainland China & Asia-Pacific)
If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1zTfABHEkr/
I will continue updating model resources on Quark Drive:
👉 https://pan.quark.cn/s/20c6f6f8d87b
These resources are mainly prepared for local users, making creation and learning more convenient.
⚙️ 在线体验工作流
👉 工作流： https://www.runninghub.ai/post/2025493219651362817?inviteCode=rh-v1111
打开上方链接即可直接运行该工作流，实时查看生成效果。
如果觉得效果理想，你也可以在本地进行自定义部署。
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📺 Bilibili 更新（中国大陆及南亚太地区）
如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。
📺 B站视频： https://www.bilibili.com/video/BV1zTfABHEkr/
我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。