MoCha - Replace Anyone in a Video with ComfyUI

ComfyUI Workflow: MoCha for Easy, Realistic Video Character Replacement

MoCha is a ComfyUI workflow that lets you swap anyone in a video with a new character using just three inputs: a source video, a single first‑frame mask, and a few reference images, producing natural results without pose or depth maps. It preserves lighting, color tone, motion, and expressions so the replacement feels seamless even in complex scenes.

What makes MoCha special

• Minimal setup: One first‑frame mask and a small set of reference images are all you need, no per‑frame masks or extra structural guidance.
• Natural integration: Replacements keep the scene’s original lighting and color tone, blending convincingly with camera motion and environment.
• Robust in tough shots: Handles occlusions, rare poses, fast movements, and object interactions with fewer artifacts and better temporal stability.
• State‑of‑the‑art quality: Extensive experiments show MoCha substantially outperforms prior methods on consistency and fidelity.

How it works

• Unified token stream: MoCha re‑renders the target by unifying video frames, references, and the mask into a single token stream for stable temporal synthesis.
• Condition‑aware RoPE: Condition‑aware positional encoding supports multi‑reference images and variable‑length video generation.
• Stronger training data: A synthetic data pipeline provides qualified paired videos to improve real‑world robustness.

Quick start in ComfyUI

• Inputs: Source video, a precise first‑frame mask, and 2–5 clean reference images covering key angles.
• Load workflow: Open the MoCha ComfyUI graph, connect video, mask, and reference inputs, and set your prompts or labels as needed.
• Generate: Run inference to produce the edited clip with your selected character replacement.

Recommended settings

• References: Use varied angles with at least one sharp front‑facing image to stabilize identity across frames.
• Masking: Invest in an accurate first‑frame mask—MoCha propagates structure without per‑frame work.
• Lighting: Choose references whose lighting roughly matches the shot for the most natural integration.
• Clip length: Start with shorter segments to validate identity and motion before rendering longer shots.

Why use this workflow?

• Faster from idea to output: Skip pose/depth rigs and frame‑by‑frame masking to iterate quickly.
• Fewer artifacts over time: Unified tokenization and condition‑aware design improve temporal coherence in challenging footage.
• Cinematic realism: Preserves scene lighting, color tone, motion, and expressions for convincing swaps in real and stylized videos.

Use cases

• Filmmaking and VFX: Replace performers while retaining on‑set lighting and camera motion for previz or final shots.
• Creator edits: Swap faces for privacy, parody, or branded content while keeping natural performance.
• IP‑safe prototyping: Test character ideas and styles without reshoots or mocap setups.
• Stylized recasts: Condition on cartoon or stylized references to re‑imagine scenes with a new visual identity.

Step‑by‑step (reference flow)

• Connect inputs: Source video, first‑frame mask, and reference images to the MoCha nodes in your ComfyUI graph.
• Set conditions: Ensure references and mask feed the MoCha condition encoder for unified token streaming.
• Render: Run the sampler to synthesize the edited sequence, then export for review or side‑by‑side comparison.

Pro tips

• Mask once, reap benefits: A precise first‑frame mask reduces cleanup and improves consistency throughout the clip.
• Quality over quantity: High‑quality, varied references matter more than a large number of similar frames.
• Tough lighting is okay: MoCha handles shaking lights and strong backlight, but matched references reduce halos on edges.
• Iterate in chunks: Validate short shots first for faster feedback, then scale to full sequences.

FAQ

• Do I need pose or depth maps?
  No. MoCha only needs a first‑frame mask and reference images.
• Does it work on real people and cartoons?
  Yes. It performs strongly on real‑person and stylized/cartoon characters.
• How does it keep motion consistent?
  Unified token stream and condition‑aware RoPE stabilize identity and motion across frames. Conclusion MoCha brings simple three‑input character replacement to ComfyUI with natural lighting, strong identity consistency, and robust performance in difficult scenes, making high‑fidelity swaps practical for creators and productions alike.