{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-klingteam--liveportrait","slug":"klingteam--liveportrait","name":"LivePortrait","type":"webapp","url":"https://huggingface.co/spaces/KlingTeam/LivePortrait","page_url":"https://unfragile.ai/klingteam--liveportrait","categories":["image-generation"],"tags":["gradio","Multimodal","Motion control","Image-to-Video","Video-to-Video","language models","LLMs","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-klingteam--liveportrait__cap_0","uri":"capability://image.visual.portrait.to.video.animation.with.facial.reenactment","name":"portrait-to-video animation with facial reenactment","description":"Transforms a static portrait image into an animated video by applying facial motion control derived from a reference video or motion sequence. Uses deep learning-based facial landmark detection and motion transfer to map head pose, eye gaze, and expression changes from a source onto the target portrait while preserving identity and photorealism. The system operates through a multi-stage pipeline: facial analysis → motion extraction → neural rendering with identity preservation constraints.","intents":["Create talking head videos from a single portrait photo without recording","Generate personalized video messages with custom facial expressions and head movements","Automate avatar animation for virtual presenters or digital humans","Apply realistic facial motion to historical portraits or artwork"],"best_for":["Content creators producing video content at scale without talent","Teams building virtual avatar systems or digital human applications","Marketing teams generating personalized video messages","Researchers in facial animation and motion transfer"],"limitations":["Requires clear, frontal-facing portrait images for optimal results; extreme angles or occlusions degrade quality","Motion transfer fidelity depends on reference video quality and facial similarity between source and target","Computational cost scales with output video resolution and duration; real-time processing limited to lower resolutions","Identity preservation may fail with significant lighting variations or artistic/stylized portraits","No built-in lip-sync to audio; requires external audio-to-motion synthesis for synchronized speech"],"requires":["Input portrait image (JPEG, PNG, WebP; minimum 256x256 resolution recommended)","Reference video or motion sequence (MP4, WebM; 15-30 FPS optimal)","GPU with 4GB+ VRAM for inference (CPU fallback available but slow)","Modern web browser with WebGL 2.0 support for Gradio interface"],"input_types":["image (portrait photo)","video (reference motion source)"],"output_types":["video (MP4 or WebM format)"],"categories":["image-visual","motion-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_1","uri":"capability://image.visual.video.to.video.facial.motion.transfer","name":"video-to-video facial motion transfer","description":"Extracts facial motion, head pose, and expression parameters from a source video and applies them to a target portrait or video, enabling motion reuse across different identities. The system performs temporal facial landmark tracking across video frames, computes motion deltas (rotation, translation, expression coefficients), and applies these transformations to the target through a neural renderer that maintains target identity while adopting source motion patterns.","intents":["Reuse facial expressions and head movements from one person's video on another person's portrait","Create synchronized multi-person videos where all participants mirror the same facial expressions","Generate training data for facial animation models by transferring motion across identities","Adapt celebrity or influencer performances to different characters or avatars"],"best_for":["Video production teams needing motion capture without specialized equipment","Game developers creating realistic NPC facial animations","Deepfake researchers and content creators","Animation studios automating secondary character animation"],"limitations":["Temporal consistency degrades with fast head movements or rapid expression changes due to landmark tracking jitter","Requires sufficient facial visibility in source video; occlusions (masks, hands) cause motion artifacts","Cross-identity motion transfer quality depends on facial similarity; extreme morphology differences produce uncanny results","No automatic audio-visual synchronization; output video motion may drift from input audio timing","Batch processing of long videos (>5 min) requires significant GPU memory or sequential processing with latency"],"requires":["Source video with visible face (MP4, WebM, AVI; 24-60 FPS)","Target portrait image or video (same format requirements as portrait-to-video capability)","GPU with 6GB+ VRAM for smooth processing","Stable internet connection for HuggingFace Spaces inference"],"input_types":["video (source motion reference)","image or video (target identity)"],"output_types":["video (MP4 or WebM)"],"categories":["image-visual","motion-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_2","uri":"capability://image.visual.real.time.facial.landmark.detection.and.tracking","name":"real-time facial landmark detection and tracking","description":"Detects and tracks facial landmarks (eyes, nose, mouth, jaw, face contour) across video frames in real-time, computing temporal consistency through Kalman filtering or optical flow constraints. Outputs 2D or 3D landmark coordinates and head pose (pitch, yaw, roll) that serve as input for downstream motion transfer or animation tasks. Uses lightweight CNN or transformer-based detectors optimized for inference speed on consumer GPUs.","intents":["Extract precise facial geometry for motion analysis without manual annotation","Monitor facial expressions and head pose for interactive applications","Provide ground truth for training facial animation models","Enable real-time facial feature tracking for augmented reality overlays"],"best_for":["Developers building interactive facial animation systems","Researchers collecting facial motion datasets","AR/VR applications requiring real-time face tracking","Quality assurance teams validating animation output"],"limitations":["Accuracy degrades with extreme head poses (>60° yaw/pitch) or occlusions (glasses, masks, hair)","Temporal jitter in landmark positions requires post-processing smoothing; raw output exhibits frame-to-frame noise","2D landmark detection lacks depth information; 3D reconstruction requires calibration or stereo input","Performance scales linearly with number of faces in frame; multi-face tracking adds latency","No built-in handling of expression-dependent landmark variations (e.g., mouth shape changes with phonemes)"],"requires":["Video input with visible face (minimum 64x64 face region)","GPU with 2GB+ VRAM (CPU inference possible but <5 FPS)","OpenCV or similar library for frame preprocessing"],"input_types":["video (MP4, WebM, or live camera stream)","image (single frame)"],"output_types":["structured data (landmark coordinates as JSON or CSV)","video with visualization overlay"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_3","uri":"capability://image.visual.expression.and.emotion.transfer.between.faces","name":"expression and emotion transfer between faces","description":"Analyzes facial expressions and emotional states in a source face, encodes them as expression coefficients (Action Units or latent emotion vectors), and applies these expressions to a target face while preserving target identity. Uses a disentangled representation where expression and identity are learned in separate latent spaces, enabling independent manipulation. The system leverages facial action unit (FACS) decomposition or learned emotion embeddings to ensure anatomically plausible expression transfer.","intents":["Make a neutral portrait smile, frown, or show surprise without changing identity","Transfer specific emotions from one video to another person's face","Generate diverse emotional variations of the same portrait for training datasets","Create expressive avatars that mirror user emotions in real-time"],"best_for":["Character animation studios automating expression variation","Emotion recognition researchers generating synthetic training data","Virtual avatar platforms enabling emotional expressiveness","Content creators personalizing video responses with specific emotions"],"limitations":["Expression transfer quality depends on anatomical similarity; transferring extreme expressions to different face shapes produces uncanny results","Micro-expressions and subtle emotional nuances are often lost in transfer due to quantization to discrete emotion categories","No semantic understanding of context; expressions may appear inappropriate for the video content","Requires sufficient facial visibility and lighting consistency; shadows or occlusions disrupt expression detection","Temporal coherence of transferred expressions may flicker frame-to-frame without additional smoothing"],"requires":["Source face with visible expression (image or video)","Target face with neutral or baseline expression","GPU with 4GB+ VRAM","Pre-trained emotion/expression classifier (included in LivePortrait)"],"input_types":["image (source face with expression)","image (target face)"],"output_types":["image (target face with transferred expression)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_4","uri":"capability://image.visual.head.pose.and.gaze.direction.control","name":"head pose and gaze direction control","description":"Estimates and manipulates head pose (pitch, yaw, roll) and eye gaze direction independently, enabling precise control over where a portrait 'looks' and how its head is oriented. Uses 3D face model fitting or learned pose regression to extract pose parameters, then applies inverse kinematics or neural rendering to reorient the face and eyes without distorting facial features. Supports both continuous pose interpolation and discrete pose targets.","intents":["Make a portrait look directly at camera or follow a moving target","Rotate head to simulate turning or nodding without full body animation","Generate multiple viewing angles of the same face for 3D reconstruction","Create eye contact in video calls by redirecting gaze to camera position"],"best_for":["Video conferencing applications improving eye contact perception","3D face reconstruction pipelines generating multi-view training data","Interactive avatar systems with gaze-aware interactions","Film production automating eyeline matching in post-production"],"limitations":["Extreme head rotations (>90°) require hallucinating occluded facial regions, producing artifacts or unrealistic geometry","Gaze direction control is limited to plausible eye movements; impossible gaze angles (e.g., 180° rotation) are clamped","Pose estimation accuracy degrades with non-frontal faces or extreme lighting; requires calibration for accurate results","No automatic synchronization with body pose; head-only rotation appears unnatural without corresponding shoulder/torso movement","Continuous pose interpolation may exhibit temporal discontinuities at pose boundaries"],"requires":["Portrait image with visible face (frontal or near-frontal preferred)","Target pose parameters (pitch, yaw, roll in degrees) or gaze target coordinates","GPU with 2GB+ VRAM","3D face model or learned pose predictor"],"input_types":["image (portrait)","structured data (target pose angles or gaze coordinates)"],"output_types":["image (reoriented portrait)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_5","uri":"capability://automation.workflow.batch.video.processing.with.motion.parameter.extraction","name":"batch video processing with motion parameter extraction","description":"Processes multiple videos sequentially or in parallel, extracting motion parameters (landmarks, pose, expression) from each frame and aggregating results into structured datasets. Implements frame-level parallelization where independent frames are processed concurrently on GPU, with results cached to disk to enable resumable processing of long videos. Outputs motion parameters in standardized formats (JSON, CSV) compatible with downstream animation or training pipelines.","intents":["Extract motion datasets from video collections for training facial animation models","Preprocess video libraries to enable fast motion transfer without per-video inference","Generate motion statistics across multiple videos for analysis or quality assurance","Create reusable motion libraries organized by expression, pose, or emotion type"],"best_for":["ML researchers building facial animation datasets","Animation studios preprocessing motion capture video libraries","Teams generating synthetic training data at scale","Content platforms analyzing facial motion patterns across user videos"],"limitations":["Batch processing latency scales linearly with total video duration; no built-in adaptive quality reduction for long videos","Disk I/O becomes bottleneck for very large batches (>100 GB); requires fast storage (SSD) for acceptable throughput","No automatic error recovery; failed frames in middle of batch require manual restart or custom retry logic","Memory usage grows with batch size; very large batches (>1000 videos) may require external job queue system","Temporal consistency across batch boundaries is not guaranteed; motion parameters may have frame-to-frame discontinuities"],"requires":["Video files in supported formats (MP4, WebM, AVI)","GPU with 6GB+ VRAM for parallel frame processing","Sufficient disk space for output motion parameters (typically 10-20% of input video size)","Python 3.8+ with PyTorch and OpenCV"],"input_types":["video (multiple files or directory)"],"output_types":["structured data (JSON/CSV with motion parameters per frame)","video (optional visualization with landmarks overlaid)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_6","uri":"capability://tool.use.integration.gradio.based.interactive.web.interface.with.real.time.preview","name":"gradio-based interactive web interface with real-time preview","description":"Provides a browser-based UI built with Gradio that enables users to upload images/videos, adjust motion control parameters (pose, expression, motion intensity), and preview results in real-time without coding. Implements client-side parameter validation and server-side inference orchestration, with WebSocket streaming for progressive video output rendering. Supports drag-and-drop file upload, parameter sliders for continuous control, and preset templates for common animation styles.","intents":["Enable non-technical users to create animated videos without command-line tools","Provide interactive parameter tuning for motion control without rerunning full inference","Share animations via shareable links without requiring local GPU setup","Prototype facial animation ideas quickly with visual feedback"],"best_for":["Non-technical content creators and marketers","Teams prototyping avatar systems without engineering resources","Researchers demonstrating facial animation capabilities","Educational platforms teaching video synthesis concepts"],"limitations":["Web interface latency adds 500ms-2s overhead per inference due to HTTP round-trips and server queueing","Real-time preview limited to lower resolutions (480p-720p) due to bandwidth constraints; full resolution requires download","No persistent session state; parameter selections are lost on page refresh unless manually saved","Concurrent user requests queue on single GPU; response time degrades linearly with user count","File upload size limited by server configuration (typically 500MB-2GB); very large videos require splitting","No built-in authentication; public HuggingFace Spaces instance is accessible to all users"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Internet connection with sufficient bandwidth for video streaming","No local GPU required (inference runs on HuggingFace Spaces servers)","JavaScript enabled for interactive parameter controls"],"input_types":["image (via file upload or drag-drop)","video (via file upload or drag-drop)","structured data (parameter sliders and dropdowns)"],"output_types":["video (streamed to browser or downloadable)","visualization (parameter preview or animation timeline)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_7","uri":"capability://data.processing.analysis.multi.modal.input.handling.image.and.video.fusion","name":"multi-modal input handling (image and video fusion)","description":"Accepts heterogeneous input combinations (portrait image + motion video, video + expression parameters, multiple videos for motion blending) and automatically aligns them temporally and spatially for downstream processing. Implements input validation, format conversion, and preprocessing pipelines that normalize different input modalities to a common representation. Supports frame rate conversion, resolution scaling, and temporal interpolation to handle mismatched input specifications.","intents":["Combine a static portrait with motion from multiple reference videos","Apply motion from one video and expressions from another to a target face","Blend motions from multiple sources with weighted interpolation","Handle user uploads in arbitrary formats and automatically convert to compatible specifications"],"best_for":["Systems requiring flexible input combinations for creative control","Pipelines that need to handle diverse user-provided media formats","Research applications exploring motion blending and fusion","Production workflows integrating multiple motion sources"],"limitations":["Temporal alignment of videos with different frame rates introduces interpolation artifacts or temporal aliasing","Spatial alignment of faces with different scales/positions requires face detection and registration; failures cascade to downstream tasks","Format conversion overhead adds 100-500ms latency depending on input size and target format","No semantic understanding of input compatibility; users can provide mismatched inputs (e.g., full-body video for face animation) that produce poor results","Memory usage spikes during multi-input processing; very large inputs may exceed GPU memory"],"requires":["Input files in supported formats (JPEG, PNG, MP4, WebM, AVI)","GPU with 4GB+ VRAM for multi-input processing","FFmpeg or similar library for video format conversion","Face detection model for spatial alignment"],"input_types":["image (portrait)","video (motion reference)","structured data (optional parameters for blending weights)"],"output_types":["video (fused output)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-klingteam--liveportrait__cap_8","uri":"capability://planning.reasoning.motion.intensity.and.style.control","name":"motion intensity and style control","description":"Provides parametric control over the magnitude and style of applied facial motion through scaling factors and blending weights. Enables users to dial motion intensity from 0% (no motion) to 100%+ (exaggerated motion) by scaling landmark displacement vectors, and to blend between different motion styles (e.g., subtle vs. expressive) through interpolation in motion latent space. Supports preset motion styles (e.g., 'professional', 'energetic', 'subtle') that adjust multiple parameters simultaneously.","intents":["Reduce motion intensity for subtle, professional animations","Exaggerate motion for comedic or expressive effects","Blend between different motion styles for creative control","Adapt motion to match target face's natural expression range"],"best_for":["Content creators fine-tuning animation aesthetics","Teams adapting animations for different contexts (professional vs. casual)","Researchers studying motion perception and expression intensity","Interactive applications where users control animation expressiveness"],"limitations":["Motion scaling beyond 150% produces anatomically implausible expressions or facial distortion","Intensity control is global; no per-region control (e.g., intense mouth movement with subtle eye movement)","Preset styles are fixed; no user-defined custom styles without retraining","Blending between styles may produce uncanny intermediate expressions if styles are too dissimilar","Temporal consistency may degrade with extreme intensity values due to landmark tracking instability"],"requires":["Base motion (from video or motion parameters)","Intensity scaling factor (0.0-2.0+ range)","Optional style identifier (from preset list)"],"input_types":["structured data (intensity slider, style dropdown)"],"output_types":["video (with adjusted motion intensity)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"low","permissions":["Input portrait image (JPEG, PNG, WebP; minimum 256x256 resolution recommended)","Reference video or motion sequence (MP4, WebM; 15-30 FPS optimal)","GPU with 4GB+ VRAM for inference (CPU fallback available but slow)","Modern web browser with WebGL 2.0 support for Gradio interface","Source video with visible face (MP4, WebM, AVI; 24-60 FPS)","Target portrait image or video (same format requirements as portrait-to-video capability)","GPU with 6GB+ VRAM for smooth processing","Stable internet connection for HuggingFace Spaces inference","Video input with visible face (minimum 64x64 face region)","GPU with 2GB+ VRAM (CPU inference possible but <5 FPS)"],"failure_modes":["Requires clear, frontal-facing portrait images for optimal results; extreme angles or occlusions degrade quality","Motion transfer fidelity depends on reference video quality and facial similarity between source and target","Computational cost scales with output video resolution and duration; real-time processing limited to lower resolutions","Identity preservation may fail with significant lighting variations or artistic/stylized portraits","No built-in lip-sync to audio; requires external audio-to-motion synthesis for synchronized speech","Temporal consistency degrades with fast head movements or rapid expression changes due to landmark tracking jitter","Requires sufficient facial visibility in source video; occlusions (masks, hands) cause motion artifacts","Cross-identity motion transfer quality depends on facial similarity; extreme morphology differences produce uncanny results","No automatic audio-visual synchronization; output video motion may drift from input audio timing","Batch processing of long videos (>5 min) requires significant GPU memory or sequential processing with latency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:48.012Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=klingteam--liveportrait","compare_url":"https://unfragile.ai/compare?artifact=klingteam--liveportrait"}},"signature":"QuR3guwu20Lbo5W9ulwTzk+E5LhyzuEfkHOF0HNJcbWJkc3oYx14nRejO6l3TxSnMY5YG4dJzYZvTIecslSFDg==","signedAt":"2026-06-21T20:06:27.701Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/klingteam--liveportrait","artifact":"https://unfragile.ai/klingteam--liveportrait","verify":"https://unfragile.ai/api/v1/verify?slug=klingteam--liveportrait","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}