Persona Generation
Crystal-Clear Identity
A single reference image anchors each persona, preserving identity while safely reimagining outfits, scenes, and moods.
Frame-Perfect Control
Direct pose, layout, and composition (poses, hands, camera hints) so every shot matches your storyboard—no reshoots.
Expressive, On-Cue Faces
Inject head-motion and expression signals for dramatic reactions or subtle micro-expressions on demand.
Video-Ready Consistency
Generate sequences with smooth temporal coherence (reduced flicker), maintaining identity, lighting, and style across frames.
Scalable & Brand-Safe
Reproducible pipelines ensure batch consistency, while selective masking preserves backgrounds and brand assets.
Style Transformation
Precision Mask Inpainting
Modify only the regions you choose; diffusion inpainting blends new pixels seamlessly with the original.
Style LoRA Engine
Apply style modules with adjustable strength to maintain brand-aligned aesthetics.
Stable, Local Edits
Refresh masked areas without affecting the rest. Reuse seeds for consistent variations or multi-frame edits.
Seamless Finish
Automatic edge smoothing and tone matching ensure transformed content naturally fits the original lighting and color palette.
Persona Face Control
GAN-based Universal Face Control for Streaming Video
Real-time by Design
Optimized for low-latency face control in video, delivering fast generation with high facial fidelity.
Identity-Aware Encoder
Converts the source face into a compact identity code used to guide all generated frames.
Spatio-Temporal Understanding
A fusion module reads short clips at once to keep identity stable during motion.
Sharp, Stable Frames
A spatio-temporal generator upscales outputs to produce clean, coherent results over time.
Motion-Aware Training
Objectives enforce identity, pose, and lighting consistency, while a motion-aware adversary pushes realism and sharpness.
Selected Research Foundations
Adversarial Diffusion Model for Unsupervised Domain-Adaptive Semantic Segmentation
A Latent Diffusion for Stable Frame Interpolation
Real-Time, High-Fidelity Face Identity Swapping with a Vision Foundation
ModelMagicMask: A Real-time and High-fidelity Face Swapping Method Robust to Face Pose
Subject-specific High-fidelity Identity-Aware Face Swapping Model
Morphify: How Face Control Works
1. Original Frame
Input target frame that defines pose, lighting, and background.
2. Cheek / Jaw Mask
A lower-face mask from landmarks sets the blend region while protecting the hairline/forehead.
3. Cheek / Jaw Blur
Mask-aware smoothing reduces pores and noise before composition.
4. Persona Control
Inject source persona’s identity cues (shape/texture) while preserving target pose, lighting, and context.
5. High-Precision Face Mask
Landmarks/segmentation precisely localize fusion and prevent spill into hair/background.
6. Persona Attribute Injection
Reinforce source-specific attributes (lip/eye/tone) with controllable strength for natural clarity.
7. Final Harmonization
Color matching, edge refinement, and sharpening produce photorealistic, temporally stable results.
Persona Body Control
Body-Mask–Guided Inpainting
Segment the human figure and apply edits only to the torso/limbs, preserving background integrity.
Multi-Control Conditioning
Use ControlNet-Pose for skeletal guidance and ControlNet-Depth for geometric accuracy, ensuring anatomically consistent body structure.
LCM Acceleration
Latent Consistency Model (LCM)–distilled sampling cuts diffusion steps to ~4–8 for fast, lightweight generation.
Identity/Style Transfer
Condition on ID images or embeddings to transfer apparel or appearance while maintaining target scene lighting.
Seamless Compositing
Mask-aware blending and color harmonization minimize boundaries and preserve overall coherence.
Technological Advances
High-Quality
Produces photorealistic control results with crisp boundaries, temporally stable textures, and consistent shading/pose—even under motion and partial occlusion.
Real-Time
Achieves interactive, low-latency performance on a single modern GPU via lightweight modules and minimal sampling—ideal for live streaming and on-device use.
Privacy-Safe
Uses synthetic persona proxies instead of real faces; inputs are processed ephemerally on-device, not retained, and outputs are non-invertible and unlinkable to the original subject.