{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-vinthony--sadtalker","slug":"vinthony--sadtalker","name":"SadTalker","type":"webapp","url":"https://huggingface.co/spaces/vinthony/SadTalker","page_url":"https://unfragile.ai/vinthony--sadtalker","categories":["automation"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-vinthony--sadtalker__cap_0","uri":"capability://image.visual.audio.driven.facial.animation.synthesis","name":"audio-driven facial animation synthesis","description":"Generates realistic talking head videos by analyzing audio input (speech) and mapping phonetic features to 3D facial mesh deformations. Uses a deep learning pipeline that extracts audio embeddings, predicts head pose and expression coefficients, and renders the animated face onto a source image using differentiable rendering techniques. The system maintains temporal coherence across frames by modeling sequential dependencies in motion prediction.","intents":["I want to create a talking head video from a static portrait photo and an audio file","I need to generate personalized video messages without filming","I want to animate a character's face to match speech in real-time or batch processing"],"best_for":["content creators producing video messages at scale","developers building avatar-based communication tools","teams automating video content generation for marketing or education"],"limitations":["Requires clear, intelligible audio input — heavy background noise degrades animation quality","Limited to frontal or near-frontal face poses in source images; extreme angles produce artifacts","Temporal artifacts may appear at audio segment boundaries if speech is heavily edited or has long pauses","Output video quality depends on source image resolution; low-res inputs produce pixelated results"],"requires":["Audio file in WAV, MP3, or OGG format","Source image (JPG, PNG) with clear, frontal face","GPU with 4GB+ VRAM for inference (CPU inference is extremely slow)","Modern browser with WebGL support for Gradio interface"],"input_types":["audio (WAV, MP3, OGG)","image (JPG, PNG)"],"output_types":["video (MP4)","video frames (PNG sequence)"],"categories":["image-visual","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_1","uri":"capability://image.visual.multi.modal.face.reenactment.with.expression.transfer","name":"multi-modal face reenactment with expression transfer","description":"Enables transferring facial expressions and head movements from a driving video or image sequence to a target portrait, decoupling identity from motion. The system extracts facial landmarks and 3D pose information from the driving source, computes expression deltas, and applies them to the target face while preserving identity features. Uses optical flow and landmark tracking to maintain spatial coherence during reenactment.","intents":["I want to make a portrait video where the person mimics expressions from a reference video","I need to transfer head movements and emotions from one actor to another actor's face","I want to create deepfake-style content where one person's expressions drive another person's face"],"best_for":["video editors and VFX artists doing face replacement or expression transfer","entertainment studios creating digital doubles or performance capture alternatives","researchers studying facial animation and expression modeling"],"limitations":["Requires both source and target faces to be clearly visible and frontal; profile or occluded faces fail","Expression transfer quality degrades if source and target faces have very different morphology (e.g., different age, gender, ethnicity)","Cannot transfer micro-expressions or subtle emotional nuances — only gross facial movements","Landmark detection errors accumulate over long video sequences, causing drift in alignment"],"requires":["Driving video or image sequence with clear facial landmarks","Target portrait image with frontal face","GPU with 6GB+ VRAM for real-time or near-real-time processing","Video file in MP4, MOV, or AVI format"],"input_types":["video (MP4, MOV, AVI)","image (JPG, PNG)"],"output_types":["video (MP4)","video frames (PNG sequence)"],"categories":["image-visual","animation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_2","uri":"capability://automation.workflow.batch.video.generation.with.gpu.acceleration","name":"batch video generation with gpu acceleration","description":"Processes multiple audio-image pairs or video sequences in parallel using GPU-accelerated inference, with automatic batching and memory management. The Gradio interface queues requests and distributes them across available GPU memory, with fallback to CPU for overflow. Implements frame caching and intermediate result reuse to minimize redundant computation across similar inputs.","intents":["I want to generate 100+ talking head videos in one batch job without manual intervention","I need to process a large dataset of portraits with different audio files efficiently","I want to parallelize video generation across multiple GPU cores to reduce total wall-clock time"],"best_for":["content production teams generating video at scale","researchers running large-scale experiments on facial animation","automation pipelines that need to process hundreds of videos programmatically"],"limitations":["Batch processing is limited by available GPU VRAM — large batches may require splitting into sub-batches","Queue-based processing introduces latency; individual job completion time is not guaranteed to be proportional to batch size","No built-in checkpointing or resumption — if the process crashes mid-batch, all progress is lost","Gradio interface does not expose fine-grained control over batch size, memory allocation, or GPU selection"],"requires":["GPU with 8GB+ VRAM for batch processing (4GB minimum for single inference)","Multiple audio-image pairs or video files","Stable internet connection for Gradio web interface","Browser with file upload capability"],"input_types":["audio (WAV, MP3, OGG)","image (JPG, PNG)","video (MP4, MOV, AVI)"],"output_types":["video (MP4)","video frames (PNG sequence)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_3","uri":"capability://image.visual.real.time.facial.landmark.detection.and.tracking","name":"real-time facial landmark detection and tracking","description":"Detects and tracks 468 facial landmarks (eyes, nose, mouth, face contour) across video frames using a lightweight neural network (MediaPipe or similar), enabling frame-by-frame motion analysis. Landmarks are used as input features for downstream tasks like expression transfer and pose estimation. The system maintains temporal consistency by using Kalman filtering or optical flow to smooth landmark trajectories across frames.","intents":["I want to extract precise facial geometry from a video to drive animation","I need to detect when a face is in the correct pose for animation synthesis","I want to validate that facial landmarks are stable and trackable before processing"],"best_for":["developers building facial animation pipelines that need robust landmark input","researchers analyzing facial motion and expression patterns","quality assurance teams validating input video quality before animation synthesis"],"limitations":["Landmark detection fails or becomes inaccurate for faces at extreme angles (>45° yaw/pitch)","Occlusions (glasses, hands, hair) cause landmark jitter or dropout","Temporal smoothing introduces lag — real-time tracking has ~50-100ms latency","Landmark coordinates are 2D projections; 3D pose must be inferred separately"],"requires":["Video input with clear, frontal face","GPU or CPU with sufficient compute for real-time inference (30 FPS)","MediaPipe library or equivalent landmark detector"],"input_types":["video (MP4, MOV, AVI)","image (JPG, PNG)","video stream (webcam)"],"output_types":["landmark coordinates (JSON, CSV)","annotated video with landmark overlays (MP4)","pose estimates (3D rotation/translation)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_4","uri":"capability://image.visual.3d.morphable.face.model.fitting.and.manipulation","name":"3d morphable face model fitting and manipulation","description":"Fits a parametric 3D face model (Basel Face Model or similar) to 2D facial landmarks or images, extracting identity, expression, and pose parameters. The fitting process uses optimization to minimize the difference between rendered model landmarks and detected 2D landmarks. Once fitted, the model can be manipulated by adjusting expression coefficients (smile, frown, eye closure) or pose parameters (head rotation, translation) independently.","intents":["I want to extract 3D facial geometry from a 2D image for animation","I need to separate identity from expression so I can transfer expressions between faces","I want to adjust head pose or facial expressions programmatically without re-rendering"],"best_for":["developers building facial animation systems that need explicit 3D control","researchers studying 3D face reconstruction and morphable models","VFX artists who need fine-grained control over facial parameters"],"limitations":["Model fitting is sensitive to landmark detection errors — poor landmarks produce poor 3D fits","Parametric models have limited expressiveness — cannot capture unique facial features outside the model's PCA space","Fitting optimization is slow (~1-5 seconds per image) and may converge to local minima","Model assumes Lambertian reflectance and frontal lighting — fails under extreme lighting or occlusion"],"requires":["Pre-trained 3D morphable face model (Basel Face Model, 3DMM)","Facial landmarks (from MediaPipe or similar detector)","Optimization library (PyTorch, TensorFlow, or scipy)","GPU optional but recommended for batch fitting"],"input_types":["image (JPG, PNG)","facial landmarks (JSON, CSV)"],"output_types":["3D face model parameters (identity, expression, pose coefficients)","3D mesh (OBJ, PLY)","rendered face image (PNG)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_5","uri":"capability://image.visual.differentiable.rendering.for.photorealistic.face.synthesis","name":"differentiable rendering for photorealistic face synthesis","description":"Renders 3D face models with differentiable rendering techniques (soft rasterization, neural textures) to produce photorealistic output that preserves identity and lighting from the source image. The rendering pipeline includes texture mapping, shading, and compositing operations that are fully differentiable, enabling gradient-based optimization of rendering parameters. Uses neural texture networks to capture fine details (skin texture, wrinkles) that parametric models cannot represent.","intents":["I want to render animated faces that look photorealistic, not cartoon-like","I need to preserve skin texture and lighting from the original image during animation","I want to optimize rendering parameters (lighting, texture) to match the source image"],"best_for":["developers building high-quality facial animation systems","VFX studios requiring photorealistic digital doubles","researchers working on neural rendering and inverse graphics"],"limitations":["Differentiable rendering is computationally expensive — 10-50x slower than rasterization","Neural texture networks require training on the target image, adding per-image overhead","Rendering quality depends heavily on accurate 3D geometry and pose estimation","Artifacts appear at occlusion boundaries and in regions with complex lighting"],"requires":["3D face model with texture coordinates","Differentiable rendering library (PyTorch3D, Kaolin, or custom implementation)","GPU with 8GB+ VRAM for real-time rendering","Source image for texture and lighting estimation"],"input_types":["3D face model (OBJ, PLY)","image (JPG, PNG)","pose parameters (rotation, translation)"],"output_types":["rendered image (PNG)","rendered video (MP4)","normal maps, depth maps (EXR)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_6","uri":"capability://tool.use.integration.web.based.inference.interface.with.gradio","name":"web-based inference interface with gradio","description":"Provides a browser-based UI for uploading audio and image files, configuring animation parameters, and downloading output videos. Built on Gradio, a Python framework that automatically generates web interfaces from Python functions. The interface handles file uploads, GPU resource management, and asynchronous job queuing without requiring custom frontend code. Supports real-time preview and parameter adjustment before final rendering.","intents":["I want to use SadTalker without installing software or writing code","I need a simple web interface to upload files and generate videos","I want to experiment with different parameters and see results in real-time"],"best_for":["non-technical users who want to generate talking head videos","content creators prototyping video ideas quickly","teams sharing a single SadTalker instance across multiple users"],"limitations":["Gradio interface is not optimized for high-throughput production use — single-threaded by default","File uploads are limited by browser and server timeouts (typically 30-60 seconds)","No user authentication or access control — anyone with the URL can use the instance","Parameter tuning is limited to UI controls; advanced users cannot access underlying Python API directly"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Internet connection to access HuggingFace Spaces instance","File upload capability (audio and image files)"],"input_types":["audio (WAV, MP3, OGG)","image (JPG, PNG)"],"output_types":["video (MP4)","downloadable file"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_7","uri":"capability://data.processing.analysis.audio.preprocessing.and.feature.extraction","name":"audio preprocessing and feature extraction","description":"Converts audio input to mel-spectrogram features and extracts phonetic embeddings using a pre-trained speech encoder. The preprocessing pipeline includes resampling to 16kHz, normalization, and windowing. Phonetic features are extracted using a speech recognition model (Wav2Vec, HuBERT, or similar) to capture linguistic content independent of speaker identity. These features are then used as input to the facial animation model.","intents":["I want to extract speech features from audio to drive facial animation","I need to handle audio in different formats and sample rates automatically","I want to ensure animation is synchronized with speech phonetics, not just audio energy"],"best_for":["developers building audio-driven animation systems","researchers studying speech-to-gesture or speech-to-animation mapping","audio engineers who need robust feature extraction from noisy recordings"],"limitations":["Feature extraction assumes clear speech — heavy background noise or music degrades phonetic accuracy","Resampling to 16kHz loses high-frequency information; original audio quality matters","Phonetic embeddings are language-dependent — models trained on English may not work well for other languages","Feature extraction adds ~1-2 seconds of latency per audio file"],"requires":["Audio file in WAV, MP3, or OGG format","Pre-trained speech encoder (Wav2Vec, HuBERT, or similar)","Audio processing library (librosa, torchaudio)","GPU optional but recommended for fast feature extraction"],"input_types":["audio (WAV, MP3, OGG)","raw audio samples (numpy array)"],"output_types":["mel-spectrogram (numpy array, shape: [time, frequency])","phonetic embeddings (numpy array, shape: [time, embedding_dim])","phoneme sequence (text)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-vinthony--sadtalker__cap_8","uri":"capability://planning.reasoning.temporal.coherence.and.motion.smoothing","name":"temporal coherence and motion smoothing","description":"Maintains smooth, natural motion across video frames by modeling temporal dependencies in facial animation. Uses recurrent neural networks (LSTMs or Transformers) to predict expression and pose parameters frame-by-frame, with constraints that penalize large frame-to-frame changes. Applies post-processing smoothing (Gaussian filtering, Kalman filtering) to reduce jitter and ensure physically plausible motion trajectories.","intents":["I want to generate smooth, natural-looking facial motion without jitter or discontinuities","I need to ensure head movements follow realistic physics (no sudden jerks or teleportation)","I want to reduce flickering artifacts in the animated video"],"best_for":["developers building high-quality facial animation systems","content creators who need professional-grade video output","researchers studying temporal coherence in generative models"],"limitations":["Temporal smoothing introduces latency — real-time animation requires buffering multiple frames","Over-smoothing can make animation look unnatural or robotic","Temporal models require training on diverse motion sequences; limited training data produces poor generalization","Smoothing cannot fix fundamental errors in pose or expression prediction"],"requires":["Sequence of predicted facial parameters (expression, pose)","Recurrent neural network or Transformer model","Smoothing filter (Gaussian, Kalman, or custom)","GPU optional but recommended for real-time processing"],"input_types":["facial parameters (expression, pose coefficients)","video frames (PNG sequence)"],"output_types":["smoothed facial parameters (numpy array)","smoothed video (MP4)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Audio file in WAV, MP3, or OGG format","Source image (JPG, PNG) with clear, frontal face","GPU with 4GB+ VRAM for inference (CPU inference is extremely slow)","Modern browser with WebGL support for Gradio interface","Driving video or image sequence with clear facial landmarks","Target portrait image with frontal face","GPU with 6GB+ VRAM for real-time or near-real-time processing","Video file in MP4, MOV, or AVI format","GPU with 8GB+ VRAM for batch processing (4GB minimum for single inference)","Multiple audio-image pairs or video files"],"failure_modes":["Requires clear, intelligible audio input — heavy background noise degrades animation quality","Limited to frontal or near-frontal face poses in source images; extreme angles produce artifacts","Temporal artifacts may appear at audio segment boundaries if speech is heavily edited or has long pauses","Output video quality depends on source image resolution; low-res inputs produce pixelated results","Requires both source and target faces to be clearly visible and frontal; profile or occluded faces fail","Expression transfer quality degrades if source and target faces have very different morphology (e.g., different age, gender, ethnicity)","Cannot transfer micro-expressions or subtle emotional nuances — only gross facial movements","Landmark detection errors accumulate over long video sequences, causing drift in alignment","Batch processing is limited by available GPU VRAM — large batches may require splitting into sub-batches","Queue-based processing introduces latency; individual job completion time is not guaranteed to be proportional to batch size","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.36,"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":"2026-05-24T12:16:23.325Z","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=vinthony--sadtalker","compare_url":"https://unfragile.ai/compare?artifact=vinthony--sadtalker"}},"signature":"KUWCWO5/BtIimnpXBxtlKW3LyCrygbw0sfxatDpGvi8+PTaB1JYB2UT2AKJB3rH0HMe4m7csFnfoxac5tZjvCw==","signedAt":"2026-06-20T08:24:13.211Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vinthony--sadtalker","artifact":"https://unfragile.ai/vinthony--sadtalker","verify":"https://unfragile.ai/api/v1/verify?slug=vinthony--sadtalker","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"}}