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.
Diffusion based unconditional persona generation
Diffusion based conditional persona generation
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.